Method and apparatus for neuroenhancement to enhance emotional response

ABSTRACT

A method of transplanting a desired emotional state from a donor to a recipient, comprising determining an emotional state of the donor; recording neural correlates of the emotional state of the donor who is in the desired emotional state; analyzing neural correlates of the emotional state of the donor to decode at least one of a temporal and a spatial pattern corresponding to the desirable emotional state; converting said at least one of a temporal and a spatial pattern corresponding to the desirable emotional state into a neurostimulation pattern; storing the neurostimulation pattern in the nonvolatile memory; retrieving the neurostimulation pattern from the nonvolatile memory; stimulating the recipient&#39;s brain with at least one stimulus modulated with the neurostimulation pattern to induce the desired emotional state in the recipient.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a non-provisional of, and claims benefit ofpriority from, U.S. Provisional Patent Applications No. 62/612,565,filed Dec. 31, 2017, and No. 62/660,839 filed on Apr. 20, 2018, both ofwhich are expressly incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention generally relates to the fields ofneuroenhancement, neuromodulation, neurostimulation, and brainentrainment, and, more specifically, to devices, systems, and methodsfor selectively inducing brainwave activity patterns in humans oranimals that correspond to, or enhance, an emotion or emotionalresponse.

BACKGROUND OF THE INVENTION

People often substitute an authentic experience by a replica thereof.Those who cannot visit the Louvre Museum, can look at the Mona Lisa on areproduction. Anybody who has seen the real Mona Lisa in the Louvre cantestify that the emotional experience is completely different from justlooking at a reproduction. Yet, people often substitute reproductionsfor authentic works of art, when the latter are not readily accessible.The emotional response to viewing a reproduction pales in comparison tothe emotional response to viewing an authentic piece of art in a museum.Looking at a photograph of the Grand Canyon is incomparable withexperiencing the real thing—visiting the Grand Canyon, which is abreathtaking experience. Yet, people unable to travel, often replace theauthentic experience of traveling and visiting new places with watchingvideos on the Travel Chanel or on the Internet Needless to say, watchingTV or a video on the Internet is a poor substitute for the realexperience of traveling and does not elicit the strong emotions, aperson experiences when visiting new places.

Because of lack of excitement in their daily lives people seekexcitement in the movies. Movies tend to be more immersive experiencesand can produce strong emotional responses. Many movie-goers cry whilewatching movies. A sentimental, emotionally-charged movie is referred toas a tear-jerker due to its ability to elicit a strong emotionalresponse, resulting in tears. However, the emotions experience ofwatching a movie cannot be compared with the broad range of emotionsexperienced in real life.

Recent advancements in 3D viewing technology and the emergence ofVirtual Reality (VR) devices produce more realistic representation ofreality they depict. However, even VR devices are incapable of producingemotional responses comparable to the emotions experienced in real life.

A viewer may benefit from enhanced emotional responses associated withviewing art reproductions, watching TV, movies, Internet videos, orVirtual Reality.

Some people lack certain emotions. For example, sociopathicpersonalities are incapable of experiencing emotions of empathy andcompassion. A number of neurologic, psychiatric and psychologicalpathologies may affect the ability to experience certain emotions.Patients suffering from advanced stages of Parkinson and Alzheimer'sdiseases often exhibit subdued emotional response. Patients affected byparanoid schizophrenia, brain injury, or dementia sometimes experienceCapgras delusion. They see a familiar face of a spouse or another familymember but do not experience emotional response they expect toexperience when seeing a face of a close family member, which leads themto believe that they live with an imposter that only “looks like” theirfamily member they complaint about a doppelganger living with them. Itmay be beneficial to artificially enhance the emotional response of apatient, bringing it to the normal level expected of a healthy person.

It is well known that memory retention is affected by the emotionalstate of the person. Emotionally-charged experiences are etched in thememory, whereas experiences not associated with high emotions are easilyforgotten. Artificially raising emotional levels during study maysignificantly increase the retention of the information and ease itssubsequent recall.

It has been observed in neuroscience that various emotions correlatewith different frequency and location of the brainwaves. Accordingly,inducing in a subject the brainwaves of particular frequency in aparticular location may induce and/or enhance the desired emotionalresponse.

Emotions are viewed as discrete and dimensional. The discrete frameworkclassifies emotional states as physiological and behavioralmanifestations of discrete emotions such as anger, happiness, etc. Thedimensional perspective organizes emotional states by two factors,valence (positive/negative) and arousal (calm/exciting).

Emotions are thought to be associated with different parts of the brain:

Frontal Lobe (movement of the body; personality; concentration,planning, problem solving; meaning of words; emotional reactions;speech; smell); Parietal Lobe (touch and pressure; taste; bodyawareness); Temporal Lobe (hearing; recognizing faces; emotion;long-term memory); Occipital Lobe (sight); Cerebellum (Latin for littlebrain, fine motor (muscle) control; balance and coordination (avoidobjects and keep from falling)); Limbic Lobe (controls emotions likehappiness, sadness, and love).

Each reference and document cited herein is expressly incorporatedherein by reference in its entirety, for all purposes.

Time in a Biological Matter

Almost everything in biology is subject to change over time. Thesechanges occur on many different time scales, which vary greatly. Forexample, there are evolutionary changes that affect entire populationsover time rather than a single organism. Evolutionary changes are oftenslower than a human time scale that spans many years (usually, a humanlifetime). Faster variations of the timing and duration of biologicalactivity in living organisms occur, for example, in many essentialbiological processes in everyday life: in humans and animals, thesevariations occur, for example, in eating, sleeping, mating, hibernating,migration, cellular regeneration, etc. Other fast changes may includethe transmission of a neural signal, for example, through a synapse,such as the Calyx of Held, a particularly large synapse in the auditorycentral nervous system of mammals that can reach transmissionfrequencies of up to 50 Hz. With recruitment modulation, the effectivefrequencies can be higher. A single nerve impulse can reach a speed ashigh as one hundred meters (0.06 mile) per second (Kraus, David.Concepts in Modern Biology. New York: Globe Book Company, 1969: 170.).Myelination of axons can increase the speed of transmission bysegmenting the membrane depolarization process.

Many of these changes over time are repetitive or rhythmic and aredescribed as some frequency or oscillation. The field of chronobiology,examines such periodic (cyclic) phenomena in living organisms and theiradaptation, for example, to solar and lunar-related rhythms (DeCoursey,et al. (2003).) These cycles are also known as biological rhythms. Therelated terms “chronomics” and “chronome” have been used in some casesto describe either the molecular mechanisms involved in chronobiologicalphenomena or the more quantitative aspects of chronobiology,particularly where comparison of cycles between organisms is required.Chronobiological studies include, but are not limited to, comparativeanatomy, physiology, genetics, molecular biology and behavior oforganisms within biological rhythms' mechanics (DeCoursey et al.(2003).). Other aspects include epigenetics, development, reproduction,ecology, and evolution.

The most important rhythms in chronobiology are the circadian rhythms,roughly 24-hour cycles shown by physiological processes in allorganisms. They are regulated by circadian clocks. The circadian rhythmscan be further broken down into routine cycles during the 24-hour day(Nelson R J. 2005. An Introduction to Behavioral Endocrinology. SinauerAssociates, Inc.: Massachusetts. Pg. 587.) All animals can be classifiedaccording to their activity cycles: Diumal, which describes organismsactive during daytime; Noctumal, which describes organisms active in thenight and Crepuscular, which describes animals primarily active duringthe dawn and dusk hours (e.g., white-tailed deer, some bats).

While circadian rhythms are defined as regulated by endogenousprocesses, other biological cycles may be regulated by exogenoussignals. In some cases, multi-trophic systems may exhibit rhythms drivenby the circadian clock of one of the members (which may also beinfluenced or reset by external factors).

Many other important cycles are also studied, including: Infradianrhythms, which are cycles longer than a day. Examples include circannualor annual cycles that govern migration or reproduction cycles in manyplants and animals, or the human menstrual cycle; ultradian rhythms,which are cycles shorter than 24 hours, such as the 90-minute REM cycle,the 4-hour nasal cycle, or the 3-hour cycle of growth hormoneproduction; tidal rhythms, commonly observed in marine life, whichfollow the roughly 12.4-hour transition from high to low tide and back;lunar rhythms, which follow the lunar month (29.5 days). They arerelevant, for example, to marine life, as the level of the tides ismodulated across the lunar cycle; and gene oscillations—some genes areexpressed more during certain hours of the day than during other hours.

Within each cycle, the time period during which the process is moreactive is called the acrophase (Refinetti, Roberto (2006). CircadianPhysiology. CRC Press/Taylor & Francis Group. ISBN 0-8493-2233-2. Laysummary). When the process is less active, the cycle is in itsbathyphase, or trough phase. The particular moment of highest activityis the peak or maximum; the lowest point is the nadir. How high (or low)the process gets is measured by the amplitude.

Neural Correlates

A neural correlate of an emotional or mental state is anelectro-neuro-biological state or the state assumed by some biophysicalsubsystem of the brain, whose presence necessarily and regularlycorrelates with such specific emotional or mental states. All propertiescredited to the mind, including consciousness, emotion, and desires arethought to have direct neural correlates. For our purposes, neuralcorrelates of an emotional or mental state can be defined as the minimalset of neuronal oscillations that correspond to the given emotional ormental state. Neuroscience uses empirical approaches to discover neuralcorrelates of emotional or mental state.

Mental State

A mental state is a state of mind that a subject is in. Some mentalstates are pure and unambiguous, while humans are capable of complexstates that are a combination of mental representations, which may havein their pure state contradictory characteristics. There are severalparadigmatic states of mind that a subject has: love, hate, pleasure,fear, and pain. Mental states can also include a waking state, asleeping state, a flow (or being in the “zone”), a will (desire) forsomething, and a mood (a mental state). A mental state is a hypotheticalstate that corresponds to thinking and feeling, and consists of aconglomeration of mental representations. A mental state is related toan emotion, though it can also relate to cognitive processes. Becausethe mental state itself is complex and potentially possessesinconsistent attributes, clear interpretation of mental state throughexternal analysis (other than self-reporting) is difficult orimpossible. However, some studies report that certain attributes ofmental state or thought processes may, in fact, be determined throughpassive monitoring, such as EEG, or fMRI with some degree of statisticalreliability. In most studies, the characterization of mental state wasan endpoint, and the raw signals, after statistical classification orsemantic labeling, are superseded. The remaining signal energy treatedas noise.

A number of studies report that certain attributes of mental state orthought processes may in fact be determined through passive monitoring,such as EEG, with some degree of statistical reliability. In moststudies, the characterization of mental state was an endpoint, and theraw signals, after statistically classification or semantic labelling,are superseded and the remaining signal energy treated as noise.

Brain

The brain is a key part of the central nervous system, enclosed in theskull. In humans, and mammals more generally, the brain controls bothautonomic processes, as well as cognitive processes. The brain (and to alesser extent, the spinal cord) controls all volitional functions of thebody and interprets information from the outside world. Intelligence,memory, emotions, speech, thoughts, movements and creativity arecontrolled by the brain. The central nervous system also controlsautonomic functions and many homeostatic and reflex actions, such asbreathing, heart rate, etc. The human brain consists of the cerebrum,cerebellum, and brainstem. The brainstem includes the midbrain, thepons, and the medulla oblongata. Sometimes the diencephalon, the caudalpart of the forebrain, is included.

The brain is composed of neurons, neuroglia (a.k.a., glia), and othercell types in connected networks that integrate sensory inputs, controlmovements, facilitate learning and memory, activate and expressemotions, and control all other behavioral and cognitive functions.Neurons communicate primarily through electrochemical pulses thattransmit signals between connected cells within and between brain areas.Thus, the desire to noninvasively capture and replicate neural activityassociated with cognitive states has been a subject of interest tobehavioral and cognitive neuroscientists.

Technological advances now allow for non-invasive recording of largequantities of information from the brain at multiple spatial andtemporal scales. Examples include electroencephalogram (“EEG”) datausing multi-channel electrode arrays placed on the scalp or inside thebrain, magnetoencephalography (“MEG”), magnetic resonance imaging(“MRI”), functional data using functional magnetic resonance imaging(“fMRI”), positron emission tomography (“PET”), near-infraredspectroscopy (“NIRS”), single-photon emission computed tomography(“SPECT”), and others.

Noninvasive neuromodulation technologies have also been developed thatcan modulate the pattern of neural activity, and thereby cause alteredbehavior, cognitive states, perception, and motor output Integration ofnoninvasive measurement and neuromodulation techniques for identifyingand transplanting brain states from neural activity would be veryvaluable for clinical therapies, such as brain stimulation and relatedtechnologies often attempting to treat disorders of cognition.

The brainstem provides the main motor and sensory innervation to theface and neck via the cranial nerves. Of the twelve pairs of cranialnerves, ten pairs come from the brainstem. This is an extremelyimportant part of the brain, as the nerve connections of the motor andsensory systems from the main part of the brain to the rest of the bodypass through the brainstem. This includes the corticospinal tract(motor), the posterior column-medial lemniscus pathway (fine touch,vibration sensation, and proprioception), and the spinothalamic tract(pain, temperature, itch, and crude touch). The brainstem also plays animportant role in the regulation of cardiac and respiratory function. Italso regulates the central nervous system and is pivotal in maintainingconsciousness and regulating the sleep cycle. The brainstem has manybasic functions including controlling heart rate, breathing, sleeping,and eating.

The function of the skull is to protect delicate brain tissue frominjury. The skull consists of eight fused bones: the frontal, twoparietal, two temporal, sphenoid, occipital and ethmoid. The face isformed by 14 paired bones including the maxilla, zygoma, nasal,palatine, lacrimal, inferior nasal conchae, mandible, and vomer. Thebony skull is separated from the brain by the dura, a membranous organ,which in turn contains cerebrospinal fluid. The cortical surface of thebrain typically is not subject to localized pressure from the skull. Theskull, therefore, imposes a banier to electrical access to the brainfunctions, and in a healthy human, breaching the dura to access thebrain is highly disfavored. The result is that electrical readings ofbrain activity are filtered by the dura, the cerebrospinal fluid, theskull, the scalp, hair, resulting in a loss of potential spatialresolution and amplitude of signals emanating from the brain. Whilemagnetic fields resulting from brain electrical activity are accessible,the spatial resolution using feasible sensors is also limited.

The cerebrum is the largest part of the brain and is composed of rightand left hemispheres. It performs higher functions, such as interpretinginputs from the senses, as well as speech, reasoning, emotions, leaning,and fine control of movement. The surface of the cerebrum has a foldedappearance called the cortex. The human cortex contains about 70% of thenerve cells (neurons) and gives an appearance of gray color (greymatter). Beneath the cortex are long connecting fibers between neurons,called axons, which make up the white matter.

The cerebellum is located behind the cerebrum and brainstem. Itcoordinates muscle movements, helps to maintain balance and posture. Thecerebellum may also be involved in some cognitive functions such asattention and language, as well as in regulating fear and pleasureresponses. There is considerable evidence that the cerebellum plays anessential role in some types of motor learning. The tasks where thecerebellum most clearly comes into play are those in which it isnecessary to make fine adjustments to the way an action is performed.There is a dispute about whether learning takes place within thecerebellum itself, or whether it merely serves to provide signals thatpromote learning in other brain structures. Cerebellum also plays animportant role in sleep and long-term memory formation.

The brain communicates with the body through the spinal cord and twelvepairs of cranial nerves. Ten of the twelve pairs of cranial nerves thatcontrol hearing, eye movement, facial sensations, taste, swallowing andmovement of the face, neck, shoulder and tongue muscles originate in thebrainstem. The cranial nerves for smell and vision originate in thecerebrum.

The right and left hemispheres of the brain are joined by a structureconsisting of fibers called the corpus callosum. Each hemispherecontrols the opposite side of the body. The right eye sends visualsignals to the left hemisphere and vice versa. However, the right earsends signals to the right hemisphere, and the left ear sends signals tothe left hemisphere. Not all functions of the hemispheres are shared.For example, speech is processed exclusively in the left hemisphere.

The cerebral hemispheres have distinct structures, which divide thebrain into lobes. Each hemisphere has four lobes: frontal, temporal,parietal, and occipital. There are very complex relationships betweenthe lobes of the brain and between the right and left hemispheres:

Frontal lobes control judgment planning, problem-solving, behavior,emotions, personality, speech, self-awareness, concentration,intelligence, body movements.

Temporal lobes control understanding of language, memory, organization,and hearing.

Parietal lobes control the interpretation of language; input fromvision, hearing, sensory, and motor temperature, pain, tactile signals,memory, spatial and visual perception.

Occipital lobes interpret visual input (movement, light, color).

A neuron is a fundamental unit of the nervous system, which comprisesthe autonomic nervous system and the central nervous system.

Brain structures and particular areas within brain structures includebut are not limited to hindbrain structures (e.g., myelencephalonstructures (e.g., medulla oblongata, medullary pyramids, olivary body,inferior olivary nucleus, respiratory center, cuneate nucleus, gracilenucleus, intercalated nucleus, medullary cranial nerve nuclei, inferiorsalivatory nucleus, nucleus ambiguous, dorsal nucleus of the vagusnerve, hypoglossal nucleus, solitary nucleus, etc.), metencephalonstructures (e.g., pons, pontine cranial nerve nuclei, chief or pontinenucleus of the trigeminal nerve sensory nucleus (V), motor nucleus forthe trigeminal nerve (V), abducens nucleus (VI), facial nerve nucleus(VII), vestibulocochlear nuclei (vestibular nuclei and cochlear nuclei)(VIII), superior salivatory nucleus, pontine tegmentum, respiratorycenters, pneumotaxic center, apneustic center, pontine micturitioncenter (Banington's nucleus), locus coeruleus, pedunculopontine nucleus,laterodorsal tegmental nucleus, tegmental pontine reticular nucleus,superior olivary complex, paramedian pontine reticular formation,cerebellar peduncles, superior cerebellar peduncle, middle cerebellarpeduncle, inferior cerebellar peduncle, fourth ventricle, cerebellum,cerebellar vermis, cerebellar hemispheres, anterior lobe, posteriorlobe, flocculonodular lobe, cerebellar nuclei, fastigial nucleus,interposed nucleus, globose nucleus, emboliform nucleus, dentatenucleus, etc.)), midbrain structures (e.g., tectum, corporaquadrigemina, inferior colliculi, superior colliculi, pretectum,tegmentum, periaqueductal gray, parabrachial area, medial parabrachialnucleus, lateral parabrachial nucleus, subparabrachial nucleus(Kolliker-Fuse nucleus), rostral interstitial nucleus of mediallongitudinal fasciculus, midbrain reticular formation, dorsal raphenucleus, red nucleus, ventral tegmental area, substantia nigra, parscompacta, pars reticulata, interpeduncular nucleus, cerebral peduncle,cms cerebri, mesencephalic cranial nerve nuclei, oculomotor nucleus(III), trochlear nucleus (IV), mesencephalic duct (cerebral aqueduct,aqueduct of sylvius), etc.), forebrain structures (e.g., diencephalon,epithalamus structures (e.g., pineal body, habenular nuclei, stiamedullares, taenia thalami, etc.), third ventricle, thalamus structures(e.g., anterior nuclear group, anteroventral nucleus (a.k.a. ventralanterior nucleus), anterodorsal nucleus, anteromedial nucleus, medialnuclear group, medial dorsal nucleus, midline nuclear group, paratenialnucleus, reuniens nucleus, rhomboidal nucleus, intralaminar nucleargroup, centromedial nucleus, parafascicular nucleus, paracentralnucleus, central lateral nucleus, central medial nucleus, lateralnuclear group, lateral dorsal nucleus, lateral posterior nucleus,pulvinar, ventral nuclear group, ventral anterior nucleus, ventrallateral nucleus, ventral posterior nucleus, ventral posterior lateralnucleus, ventral posterior medial nucleus, metathalamus, medialgeniculate body, lateral geniculate body, thalamic reticular nucleus,etc.), hypothalamus structures (e.g., anterior, medial area, parts ofpreoptic area, medial preoptic nucleus, suprachiasmatic nucleus,paraventricular nucleus, supraoptic nucleus (mainly), anteriorhypothalamic nucleus, lateral area, parts of preoptic area, lateralpreoptic nucleus, anterior part of lateral nucleus, part of supraopticnucleus, other nuclei of preoptic area, median preoptic nucleus,periventricular preoptic nucleus, tuberal, medial area, dorsomedialhypothalamic nucleus, ventromedial nucleus, arcuate nucleus, lateralarea, tuberal part of lateral nucleus, lateral tuberal nuclei,posterior, medial area, mammillary nuclei (part of mammillary bodies),posterior nucleus, lateral area, posterior part of lateral nucleus,optic chiasm, subfomical organ, periventricular nucleus, pituitarystalk, tuber cinereum, tuberal nucleus, tuberomammillary nucleus,tuberal region, mammillary bodies, mammillary nucleus, etc.),subthalamus structures (e.g., thalamic nucleus, zona incerta, etc.),pituitary gland structures (e.g., neurohypophysis, pars intermedia(intermediate lobe), adenohypophysis, etc.), telencephalon structures,white matter structures (e.g., corona radiata, internal capsule,external capsule, extreme capsule, arcuate fasciculus, uncinatefasciculus, perforant path, etc.), subcortical structures (e.g.,hippocampus (medial temporal lobe), dentate gyrus, comu ammonis (CAfields), comu ammonis area 1, comu ammonis area 2, comu ammonis area 3,comu ammonis area 4, amygdala (limbic system) (limbic lobe), centralnucleus (autonomic nervous system), medial nucleus (accessory olfactorysystem), cortical and basomedial nuclei (main olfactory system),lateral) and basolateral nuclei (frontotemporal cortical system),claustrum, basal ganglia, striatum, dorsal striatum (a.k.a.neostriatum), putamen, caudate nucleus, ventral striatum, nucleusaccumbens, olfactory tubercle, globus pallidus (forms nucleuslentiformis with putamen), subthalamic nucleus, basal forebrain,anterior perforated substance, substantia innominata, nucleus basalis,diagonal band of Broca, medial septal nuclei, etc.), rhinencephalonstructures (e.g., olfactory bulb, piriform cortex, anterior olfactorynucleus, olfactory tract, anterior commissure, uncus, etc.), cerebralcortex structures (e.g., frontal lobe, cortex, primary motor cortex(precentral gyrus, M1), supplementary motor cortex, premotor cortex,prefrontal cortex, gyri, superior frontal gyrus, middle frontal gyrus,inferior frontal gyrus, Brodmann areas: 4, 6, 8, 9, 10, 11, 12, 24, 25,32, 33, 44, 45, 46, 47, parietal lobe, cortex, primary somatosensorycortex (S1), secondary somatosensory cortex (S2), posterior parietalcortex, gyri, postcentral gyrus (primary somesthetic area), precuneus,Brodmann areas 1, 2, 3 (primary somesthetic area); 5, 7, 23, 26, 29, 31,39, 40, occipital lobe, cortex, primary visual cortex (V1), V2, V3, V4,V5/MT, lateral occipital gyrus, cuneus, Brodmann areas 17 (V1, primaryvisual cortex); 18, 19, temporal lobe, primary auditory cortex (A1),secondary auditory cortex (A2), inferior temporal cortex, posteriorinferior temporal cortex, superior temporal gyrus, middle temporalgyrus, inferior temporal gyrus, entorhinal cortex, perirhinal cortex,parahippocampal gyrus, fusiform gyrus, Brodmann areas: 9, 20, 21, 22,27, 34, 35, 36, 37, 38, 41, 42, medial superior temporal area (MST),insular cortex, cingulate cortex, anterior cingulate, Posteriorcingulate, Retrosplenial cortex, Indusium griseum, Subgenual area 25,Brodmann areas 23, 24; 26, 29, 30 (retrosplenial areas); 31, 32, etc.).

The brain is the largest sex organ controlling the biological urge,mediating all thoughts, experiences and physiological responses to sex.The euphoric and pleasurable experience of sex stems primarily from thelimbic system including the amygdala, hippocampus and limbic lobe(dentate and cingulate gyrus).

Neurons

Neurons are electrically excitable cells that receive, process, andtransmit information, and based on that information sends a signal toother neurons, muscles, or glands through electrical and chemicalsignals. These signals between neurons occur via specialized connectionscalled synapses. Neurons can connect to each other to form neuralnetworks. The basic purpose of a neuron is to receive incominginformation and, based upon that information send a signal to otherneurons, muscles, or glands. Neurons are designed to rapidly sendsignals across physiologically long distances. They do this usingelectrical signals called nerve impulses or action potentials. When anerve impulse reaches the end of a neuron, it triggers the release of achemical, or neurotransmitter. The neurotransmitter travels rapidlyacross the short gap between cells (the synapse) and acts to signal theadjacent cell. Seewww.biologyreference.com/Mo-Nu/Neuron.html#ixzz5AVxCuM5a.

Neurons can receive thousands of inputs from other neurons throughsynapses. Synaptic integration is a mechanism whereby neurons integratethese inputs before the generation of a nerve impulse, or actionpotential. The ability of synaptic inputs to effect neuronal output isdetermined by a number of factors: Size, shape and relative timing ofelectrical potentials generated by synaptic inputs; the geometricstructure of the target neuron; the physical location of synaptic inputswithin that structure; and the expression of voltage-gated channels indifferent regions of the neuronal membrane.

Neurons within a neural network receive information from, and sendinformation to, many other cells, at specialized junctions calledsynapses. Synaptic integration is the computational process by which anindividual neuron processes its synaptic inputs and converts them intoan output signal. Synaptic potentials occur when neurotransmitter bindsto and opens ligand-operated channels in the dendritic membrane,allowing ions to move into or out of the cell according to theirelectrochemical gradient Synaptic potentials can be either excitatory orinhibitory depending on the direction and charge of ion movement. Actionpotentials occur if the summed synaptic inputs to a neuron reach athreshold level of depolarization and trigger regenerative opening ofvoltage-gated ion channels. Synaptic potentials are often brief and ofsmall amplitude, therefore summation of inputs in time (temporalsummation) or from multiple synaptic inputs (spatial summation) isusually required to reach action potential firing threshold.

There are two types of synapses: electrical synapses and chemicalsynapses. Electrical synapses are a direct electrical coupling betweentwo cells mediated by gap junctions, which are pores constructed ofconnexin proteins—essentially result in the passing of a gradientpotential (may be depolarizing or hyperpolarizing) between two cells.Electrical synapses are very rapid (no synaptic delay). It is a passiveprocess where signal can degrade with distance and may not produce alarge enough depolarization to initiate an action potential in thepostsynaptic cell. Electrical synapses are bidirectional, i.e.,postsynaptic cell can actually send messages to the “presynaptic cell.

Chemical synapses are a coupling between two cells throughneuro-transmitters, ligand or voltage gated channels, receptors. Theyare influenced by the concentration and types of ions on either side ofthe membrane. Among the neurotransmitters, Glutamate, sodium, potassium,and calcium are positively charged. G ABA and chloride are negativelycharged. Neurotransmitter junctions provide an opportunity forpharmacological intervention, and many different drugs, includingillicit drugs, act at synapses.

An excitatory postsynaptic potential (EPSP) is a postsynaptic potentialthat makes the postsynaptic neuron more likely to fire an actionpotential. An electrical charge (hyperpolarization) in the membrane of apostsynaptic neuron is caused by the binding of an inhibitoryneurotransmitter from a presynaptic cell to a postsynaptic receptor. Itmakes it more difficult for a postsynaptic neuron to generate an actionpotential. An electrical change (depolarization) in the membrane of apostsynaptic neuron caused by the binding of an excitatoryneurotransmitter from a presynaptic cell to a postsynaptic receptor. Itmakes it more likely for a postsynaptic neuron to generate an actionpotential. In a neuronal synapse that uses glutamate as receptor, forexample, receptors open ion channels that are non-selectively permeableto cations. When these glutamate receptors are activated, both Na+ andK+flow across the postsynaptic membrane. The reversal potential (Erev)for the post-synaptic current is approximately 0 mV. The restingpotential of neurons is approximately −60 mV. The resulting EPSP willdepolarize the post synaptic membrane potential, bringing it toward 0mV.

An inhibitory postsynaptic potential (IPSP) is a kind of synapticpotential that makes a postsynaptic neuron less likely to generate anaction potential. An example of inhibitory post synaptic action is aneuronal synapse that uses γ-Aminobutyric acid (GABA) as itstransmitter. At such synapses, the G ABA receptors typically openchannels that are selectively permeable to Cl—. When these channelsopen, negatively charged chloride ions can flow across the membrane. Thepostsynaptic neuron has a resting potential of −60 mV and an actionpotential threshold of −40 mV. Transmitter release at this synapse willinhibit the postsynaptic cell. Since ECl is more negative than theaction potential threshold, e.g., −70 mV, it reduces the probabilitythat the postsynaptic cell will fire an action potential.

Some types of neurotransmitters, such as glutamate, consistently resultin EPSPs. Others, such as G ABA, consistently result in IPSPs. Theaction potential lasts about one millisecond (1 msec). In contrast, theEPSPs and IPSPs can last as long as 5 to 10 msec. This allows the effectof one postsynaptic potential to build upon the next and so on.

Membrane leakage, and to a lesser extent, potentials per se, can beinfluenced by external electrical and magnetic fields. These fields maybe generated focally, such as through implanted electrodes, or lessspecifically, such as through transcranial stimulation. Transcranialstimulation may be subthreshold or superthreshold. In the former case,the external stimulation acts to modulate resting membrane potential,making nerves more or less excitable. Such stimulation may be directcurrent or alternating current. In the latter case, this will tend tosynchronize neuron depolarization with the signals. Superthresholdstimulation can be painful (at least because the stimulus directlyexcites pain neurons) and must be pulsed. Since this has correspondenceto electroconvulsive therapy, superthreshold transcranial stimulation issparingly used.

A number of neurotransmitters are known, as are pharmaceuticalinterventions and therapies that influence these compounds. Typically,the major neurotransmitters are small monoamine molecules, such asdopamine, epinephrine, norepinephrine, serotonin, G ABA, histamine,etc., as well as acetylcholine. In addition, neurotransmitters alsoinclude amino acids, gas molecules such as nitric oxide, carbonmonoxide, carbon dioxide, and hydrogen sulfide, as well as peptides. Thepresence, metabolism, and modulation of these molecules may influencelearning and memory. Supply of neurotransmitter precursors, control ofoxidative and mental stress conditions, and other influences on learningand memory-related brain chemistry, may be employed to facilitatememory, learning, and learning adaptation transfer.

The neuropeptides, as well as their respective receptors, are widelydistributed throughout the mammalian central nervous system. Duringlearning and memory processes, besides structural synaptic remodeling,changes are observed at molecular and metabolic levels with thealterations in neurotransmitter and neuropeptide synthesis and release.While there is a consensus that brain cholinergic neurotransmissionplays a critical role in the processes related to learning and memory,it is also well known that these functions are influenced by atremendous number of neuropeptides and non-peptide molecules. Argininevasopressin (AVP), oxytocin, angiotensin II, insulin, growth factors,serotonin (5-HT), melanin-concentrating hormone, histamine, bombesin andgastrin-releasing peptide (GRP), glucagon-like peptide-1 (GLP-1),cholecystokinin (CCK), dopamine, corticotropin-releasing factor (CRF)have modulatory effects on learning and memory. Among these peptides,CCK, 5-HT, and CRF play strategic roles in the modulation of memoryprocesses under stressful conditions. CRF is accepted as the mainneuropeptide involved in both physical and emotional stress, with aprotective role during stress, possibly through the activation of thehypothalamo-pituitary (HPA) axis. The peptide CCK has been proposed tofacilitate memory processing, and CCK-like immunoreactivity in thehypothalamus was observed upon stress exposure, suggesting that CCK mayparticipate in the central control of stress response and stress-inducedmemory dysfunction. On the other hand, 5-HT appears to play a role inbehaviors that involve a high cognitive demand and stress exposureactivates serotonergic systems in a variety of brain regions. See:

-   Mehmetali Gülpinar, Berrak C Yeğen, “The Physiology of Learning and    Memory: Role of Peptides and Stress”, Current Protein and Peptide    Science, 2004(5)-   www.researchgate.net/publicaion/8147320_The_Physiology_of_Learning_and_Memory_Role_of_Peptides_and_Stress.Deep    brain stimulation is described in NIH Research Matters, “A    noninvasive deep brain stimulation technique”, (2017),-   Brainworks, “QEEG Brain Mapping”.-   Carmon, A., Mor, J., & Goldberg, J. (1976). Evoked cerebral    responses to noxious thermal stimuli in humans. Experimental Brain    Research, 25(1), 103-107.

Brainwaves

At the root of all our thoughts, emotions and behaviors is thecommunication between neurons within our brains, a rhythmic orrepetitive neural activity in the central nervous system. Theoscillation can be produced by a single neuron or by synchronizedelectrical pulses from ensembles of neurons communicating with eachother. The interaction between neurons can give rise to oscillations ata different frequency than the firing frequency of individual neurons.The synchronized activity of large numbers of neurons producesmacroscopic oscillations, which can be observed in anelectroencephalogram. They are divided into bandwidths to describe theirpurported functions or functional relationships. Oscillatory activity inthe brain is widely observed at different levels of organization and isthought to play a key role in processing neural information. Numerousexperimental studies support a functional role of neural oscillations. Aunified interpretation, however, is still not determined. Neuraloscillations and synchronization have been linked to many cognitivefunctions such as information transfer, perception, motor control andmemory. Electroencephalographic (EEG) signals are relatively easy andsafe to acquire, have a long history of analysis, and can have highdimensionality, e.g., up to 128 or 256 separate recording electrodes.While the information represented in each electrode is not independentof the others, and the noise in the signals high, there is muchinformation available through such signals that has not been fullycharacterized to date.

Brainwaves have been widely studied in neural activity generated bylarge groups of neurons, mostly by EEG. In general, EEG signals revealoscillatory activity (groups of neurons periodically firing insynchrony), in specific frequency bands: alpha (7.5-12.5 Hz) that can bedetected from the occipital lobe during relaxed wakefulness and whichincreases when the eyes are closed; delta (1-4 Hz), theta (4-8 Hz), beta(13-30 Hz), low gamma (30-70 Hz), and high gamma (70-150 Hz) frequencybands, where faster rhythms such as gamma activity have been linked tocognitive processing. Higher frequencies imply multiple groups ofneurons firing in coordination, either in parallel or in series, orboth, since individual neurons do not fire at rates of 100 Hz. Neuraloscillations of specific characteristics have been linked to cognitivestates, such as awareness and consciousness and different sleep stages.

Nyquist Theorem states that the highest frequency that can be accuratelyrepresented is one-half of the sampling rate. Practically, the samplingrate should be ten times higher than the highest frequency of thesignal. (See, www.slideshare.net/ertvk/eeg-examples). While EEG signalsare largely band limited, the superimposed noise may not be. Further,the EEG signals themselves represent components from a large number ofneurons, which fire independently. Therefore, large bandwidth signalacquisition may have utility.

It is a useful analogy to think of brainwaves as music. In orchestralmusic, where various instrument groups (sting groups, such as violins,violas, cellos and double basses, brass, woodwind, and percussioninstruments) produce particular sounds bases on their respectivecharacteristic frequencies of vibrations that all come together in amusical composition. Similarly, in the brain, groups of neuronsoscillate in unison producing specific frequencies that combine inbrainwaves. Like in a symphony, the higher and lower frequencies linkand cohere with each other through harmonics, especially when oneconsiders that neurons may be coordinated not only based on transitions,but also on phase delay. Oscillatory activity is observed throughout thecentral nervous system at all levels of organization. Each respectivemental state is associated with the dominant neuro oscillationfrequency. Moreover, the nuances of each mental state may be associatedwith secondary and tertiary harmonics or, using musical analogy, the“overtones.” Some hypothesize that very slow brainwaves serve tosynchronize various lobes and neuronal groups in the brain (similarly tolaw-frequency instruments, such as drums and double basses, serve toprovide overall rhythm to the orchestra).

The functions of brainwaves are wide-ranging and vary for differenttypes of oscillatory activity. Neural oscillations also play animportant role in many neurological disorders.

Delta wave is the frequency range from 0.5 Hz to 4 Hz. It tends to bethe highest in amplitude and the slowest waves (except for very-slowwaves that have frequency less than 0.5 Hz). It is normally seen inadults in NREM (en.wikipedia.org/wiki/NREM). It is also seen normally inbabies. It may occur focally with subcortical lesions and in generaldistribution with diffuse lesions, metabolic encephalopathyhydrocephalus or deep midline lesions. It is usually most prominentfrontally in adults (e.g., FIRDA-frontal intermittent rhythmic delta)and posteriorly in children (e.g., OIRDA-occipital intermittent rhythmicdelta).

Theta is the frequency range from 4 Hz to 7 Hz. Theta is normally seenin young children. It may be seen in drowsiness or arousal in olderchildren and adults; it can also be seen in meditation. Excess theta forage represents abnormal activity. It can be seen as a focal disturbancein focal subcortical lesions; it can be seen in generalized distributionin diffuse disorder or metabolic encephalopathy or deep midlinedisorders or some instances of hydrocephalus. On the other hand, thisrange has been associated with reports of relaxed, meditative, andcreative states.

Alpha is the frequency range from 7.5 Hz to 12.5 Hz. This is the“posterior basic rhythm” (also called the “posterior dominant rhythm,”the “posterior alpha rhythm” or the Berger's wave), arising from thesynchronous and coherent electrical activity in the thalamic pacemakercells and seen in the posterior regions of the head on both sides,higher in amplitude on the dominant side. They predominantly originatefrom the occipital lobe during wakeful relaxation with closed eyes.Alpha wave emerges with the closing of the eyes and with relaxation andattenuates with eye opening or mental exertion. The posterior basicrhythm is actually slower than 8 Hz in young children (thereforetechnically in the theta range). In addition to the posterior basicrhythm, there are other normal alpha rhythms such as the sensorimotor,or mu rhythm (alpha activity in the contralateral sensory and motorcortical areas) that emerges when the hands and arms are idle; and the“third rhythm” (alpha activity in the temporal or frontal lobes). Alphacan be abnormal; for example, an EEG that has diffuse alpha occurring incoma and is not responsive to external stimuli is referred to as “alphacoma.”

Beta is the frequency range from 15 Hz to about 30 Hz. It is usuallyseen on both sides in symmetrical distribution and is most evidentfrontally. Beta activity is closely linked to motor behavior and isgenerally attenuated during active movements. Low-amplitude beta withmultiple and varying frequencies is often associated with active, busyor anxious thinking and active concentration. Rhythmic beta with adominant set of frequencies is associated with various pathologies, suchas Dup15q syndrome, and drug effects, especially benzodiazepines. It maybe absent or reduced in areas of cortical damage. It is the dominantrhythm in patients who are alert or anxious or who have their eyes open.

Gamma is the frequency range approximately 250-100 Hz. Gamma rhythms arethought to represent binding of different populations of neuronstogether into a network to carry out a certain cognitive or motorfunction. Low gamma (25-70 Hz), and high gamma (70-150 Hz) frequencybands are also recognized with higher frequencies being associated withcognitive processing.

Mu range is 8-13 Hz and partly overlaps with other frequencies, but isgenerally considered one of the two types of alpha wave (the second typebeing the third rhythm). It reflects the synchronous firing of motorneurons in a rest state. Mu suppression is thought to reflect motormirror neuron systems, because when an action is observed, the patternextinguishes, possibly because of the normal neuronal system and themirror neuron system “go out of sync” and interfere with each other.See:

-   Abeles M, Local Cortical Circuits (1982) New York: Springer-Verlag.-   Braitenberg V and Schuz A (1991) Anatomy of the Cortex. Statistics    and Geometry. New York: Springer-Verlag.-   Ebersole J S (1997) Defining epileptogenic foci: past, present,    future. J. Clin. Neurophysiology 14: 470-483.-   Edelman G M and Tononi G (2000) A Universe of Consciousness, New    York: Basic Books.-   Freeman W J (1975) Mass Action in the Nervous System, New York:    Academic Press.-   Gevins A S and Cutillo B A (1995) Neuroelectric measures of mind.    In: P L Nunez (Au), Neocortical Dynamics and Human EEG Rhythms. NY:    Oxford U. Press, pp. 304-338.-   Gevins A S, Le J, Martin N, Brickett P, Desmond J, and Reutter    B (1994) High resolution EEG: 124-channel recording, spatial    enhancement, and MRI integration methods. Electroencephalography and    Clin. Neurophysiology 90: 337-358.-   Gevins A S, Smith M E, McEvoy L and Yu D (1997) High-resolution    mapping of cortical activation related to working memory: effects of    task difficulty, type of processing, and practice. Cerebral Cortex    7: 374-385.-   Haken H (1983) Synergetics: An Introduction, 3rd Edition,    Springer-Verlag.-   Haken H (1999) What can synergetics contibute to the understanding    of brain functioning? In: Analysis of Neurophysiological Brain    Functioning, C Uhl (Ed), Berlin: Springer-Verlag, pp 7-40.-   Ingber L (1995) Statistical mechanics of multiple scales of    neocortical interactions. In: P L Nunez (Au), Neocortical Dynamics    and Human EEG Rhythms. NY: Oxford U. Press, 628-681.-   Izhikevich E M (1999) Weakly connected quasi-periodic oscillators,    FM interactions, and multiplexing in the brain, SIAM J. Applied    Mathematics 59: 2193-2223.-   Jirsa V K and Haken H (1997) A derivation of a macroscopic field    theory of the brain from the quasi-microscopic neural dynamics.    Physica D 99: 503-526.-   Jirsa V K and Kelso J A S (2000) Spatiotemporal pattern formation in    continuous systems with heterogeneous connection topologies.    Physical Review E 62: 8462-8465.-   Katznelson R D (1981) Normal modes of the brain: Neuroanatomical    basis and a physiological theoretical model. In PL Nunez (Au),    Electric Fields of the Brain: The Neurophysics of EEG, 1st Edition,    NY: Oxford U. Press, pp 401-442.-   Klimesch W (1996) Memory processes, brain oscillations and EEG    synchronization. International J. Psychophysiology 24:61-100.-   Law S K, Nunez P L and Wijesinghe R S (1993) High resolution EEG    using spline generated surface Laplacians on spherical and    ellipsoidal surfaces. IEEE Transactions on Biomedical Engineering    40: 145-153.-   Liley D T J, Cadusch P J and Dafilis M P (2002) A spatially    continuous mean field theory of electrocortical activity network.    Computation in Neural Systems 13: 67-113.-   Malmuvino J and Plonsey R (1995) Bioelectromagetism. NY: Oxford U.    Press.-   Niedermeyer E and Lopes da Silva F H (Eds) (2005)    Electroencephalography. Basic Principals, Clin. Applications, and    Related Fields. Fifth Edition. London: Williams and Wilkins.-   Nunez P L (1989) Generation of human EEG by a combination of long    and short range neocortical interactions. Brain Topography 1:    199-215.-   Nunez P L (1995) Neocortical Dynamics and Human EEG Rhythms. NY:    Oxford U. Press.-   Nunez P L (2000) Toward a large-scale quantitative description of    neocortical dynamic function and EEG (Target article), Behavioral    and Brain Sciences 23: 371-398.-   Nunez P L (2000) Neocortical dynamic theory should be as simple as    possible, but not simpler (Response to 18 commentaries on target    article), Behavioral and Brain Sciences 23: 415437.-   Nunez P L (2002) EEG. In VS Ramachandran (Ed) Encyclopedia of the    Human Brain, La Jolla: Academic Press, 169-179.-   Nunez P L and Silberstein R B (2001) On the relationship of synaptic    activity to macroscopic measurements: Does co-registration of EEG    with fMRI make sense? Brain Topog. 13:79-96.-   Nunez P L and Srinivasan R (2006) Electric Fields of the Brain: The    Neurophysics of EEG, 2nd Edition, NY: Oxford U. Press.-   Nunez P L and Srinivasan R (2006) A theoretical basis for standing    and traveling brain waves measured with human EEG with implications    for an integrated consciousness. Clin. Neurophysiology 117:    2424-2435.-   Nunez P L, Srinivasan R, Westdorp A F, Wijesinghe R S, Tucker D M,    Silberstein R B, and Cadusch P J (1997) EEG coherency I: Statistics,    reference electrode, volume conduction, Laplacians, cortical    imaging, and interpretation at multiple scales.    Electroencephalography and Clin. Neurophysiology 103: 516-527.-   Nunez P L, Wingeier B M and Silberstein R B (2001) Spatial-temporal    structures of human alpha rhythms: theory, micro-current sources,    multiscale measurements, and global binding of local networks, Human    Brain Mapping 13: 125-164.-   Nuwer M (1997) Assessment of digital EEG, quantitative EEG, and EEG    brain mapping: report of the American Academy of Neurology and the    American Clin. Neurophysiology Society. Neurology 49: 277-292.-   Penfield W and Jasper H D (1954) Epilepsy and the Functional Anatomy    of the Human Brain. London: Little, Brown and Co.-   Robinson P A, Rennie C J, Rowe D L and O'Conner S C (2004)    Estimation of multiscale neurophysiologic parameters by    electroencephalographic means. Human Brain Mapping 23: 53-72.-   Scott A C (1995) Stairway to the Mind. New York: Springer-Verlag.-   Silberstein R B, Danieli F and Nunez P L (2003) Fronto-parietal    evoked potential synchronization is increased during mental    rotation, NeuroReport 14: 67-71.-   Silberstein R B, Song J, Nunez P L and Park W (2004) Dynamic    sculpting of brain functional connectivity is correlated with    performance, Brain Topography 16: 240-254.-   Srinivasan R and Petrovic S (2006) MEG phase follows conscious    perception during binocular rivalry induced by visual stream    segregation. Cerebral Cortex, 16: 597-608.-   Srinivasan R, Nunez P L and Silberstein R B (1998) Spatial filtering    and neocortical dynamics: estimates of EEG coherence. IEEE Trans. on    Biomedical Engineering, 45: 814-825.-   Srinivasan R, Russell D P, Edelman G M, and Tononi G (1999)    Frequency tagging competing stimuli in binocular rivalry reveals    increased synchronization of neuromagnetic responses during    conscious perception. J. Neuroscience 19: 5435-5448.-   Uhl C (Ed) (1999) Analysis of Neurophysiological Brain Functioning.    Bedin: Springer-Verlag,-   Wingeier B M, Nunez P L and Silberstein R B (2001) Spherical    harmonic decomposition applied to spatial-temporal analysis of human    high-density electroencephalogram. Physical Review E 64: 051916-1 to    9.-   en.wikipedia.org/wiki/Electroencephalography

TABLE 1 Comparison of EEG bands Freq. Band (Hz) Location NormallyPathologically Delta <4 frontally in adults, adult slow-wave sleepsubcortical lesions posteriorly in in babies diffuse lesions children;high- Has been found during metabolic encephalopathy hydrocephalusamplitude waves some continuous- deep midline lesions attention tasksTheta 4-7 Found in locations higher in young children focal subcorticallesions not related to task drowsiness in adults metabolicencephalopathy at hand and teens deep midline disorders idling someinstances of hydrocephalus Associated with inhibition of elicitedresponses (has been found to spike in situations where a person isactively trying to repress a reponse or action). Alpha 7.5- posteriorregions relaxed/reflecting Coma 12.5 of head, both closing the eyessides, higher in Also associated with amplitude on inhibition control,dominant side. seemingly with the Central sites (c3- purpose of timingc4) at rest inhibitory activity in different locations across the brain.Beta 12.5- both sides, range span: active calm → Benzodiazepines(en.wikipedia.org/wiki/Benzodiazepines) 30 symmetrical intense →stressed → Dup 15q syndrome distribution, most mild obsessive evidentfrontally; active thinking, focus, low-amplitude high alert, anxiouswaves Gamma 25- Somatosensory Displays during cross- A decrease ingamma-band activity may be associated with 100 cortex modal sensorycognitive decline, especially when related to the theta band; processing(perception however, this has not been proven for use as a clinical thatcombines two diagnostic measurement different senses, such as sound andsight) Also is shown during short-term memory matching of recognizedobjects, sounds, or tactile sensations Mu 8-12 Sensorimotor Showsrest-state motor Mu suppression could indicate that motor mirrorneutrons cortex neurons. are working. Deficits in Mu suppression, andthus in mirror neurons, might play a role in autism.

EEG AND qEEG

An EEG electrode will mainly detect the neuronal activity in the brainregion just beneath it. However, the electrodes receive the activityfrom thousands of neurons. One square millimeter of cortex surface, forexample, has more than 100,000 neurons. It is only when the input to aregion is synchronized with electrical activity occurring at the sametime that simple periodic waveforms in the EEG become distinguishable.The temporal pattern associated with specific brainwaves can bedigitized and encoded a non-transient memory, and embodied in orreferenced by, computer software.

EEG (electroencephalography) and MEG (magnetoencephalography) areavailable technologies to monitor brain electrical activity. Eachgenerally has sufficient temporal resolution to follow dynamic changesin brain electrical activity. Electroencephalography (EEG) andquantitative electroencephalography (qEEG) are electrophysiologicalmonitoring methods that analyze the electrical activity of the brain tomeasure and display patterns that correspond to cognitive states and/ordiagnostic information. It is typically noninvasive, with the electrodesplaced on the scalp, although invasive electrodes are also used in somecases. EEG signals may be captured and analyzed by a mobile device,often referred as “brain wearables”. There are a variety of “brainwearables” readily available on the market today. EEGs can be obtainedwith a non-invasive method where the aggregate oscillations of brainelectric potentials are recorded with numerous electrodes attached tothe scalp of a person. Most EEG signals originate in the brain's outerlayer (the cerebral cortex), believed largely responsible for ourthoughts, emotions, and behavior. Cortical synaptic action generateselectrical signals that change in the 10 to 100-millisecond range.Transcutaneous EEG signals are limited by the relatively insulatingnature of the skull surrounding the brain, the conductivity of thecerebrospinal fluid and brain tissue, relatively low amplitude ofindividual cellular electrical activity, and distances between thecellular current flows and the electrodes. EEG is characterized by: (1)Voltage; (2) Frequency; (3) Spatial location; (4) Inter-hemisphericsymmetries; (5) Reactivity (reaction to state change); (6) Character ofwaveform occurrence (random, serial, continuous); and (7) Morphology oftransient events. EEGs can be separated into two main categories.Spontaneous EEG which occur in the absence of specific sensory stimuliand evoked potentials (EPs) which are associated with sensory stimulilike repeated light flashes, auditory tones, finger pressure or mildelectric shocks. The latter is recorded for example by time averaging toremove effects of spontaneous EEG. Non-sensory triggered potentials arealso known. EP's typically are time synchronized with the trigger, andthus have an organization principle. Event-related potentials (ERPs)provide evidence of a direct link between cognitive events and brainelectrical activity in a wide range of cognitive paradigms. It hasgenerally been held that an ERP is the result of a set of discretestimulus-evoked brain events. Event-related potentials (ERPs) arerecorded in the same way as EPs, but occur at longer latencies from thestimuli and are more associated with an endogenous brain state.

In standard EEG recording practice, 19 recording electrodes are placeduniformly on the scalp (the International 10-20 System). In addition,one or two reference electrodes (often placed on earlobes) and a groundelectrode (often placed on the nose to provide amplifiers with referencevoltages) are required. However, additional electrodes may add minimaluseful information unless supplemented by computer algorithms to reduceraw EEG data to a manageable form. When large numbers of electrodes areemployed, the potential at each location may be measured with respect tothe average of all potentials (the common average reference), whichoften provides a good estimate of potential at infinity. The commonaverage reference is not appropriate when electrode coverage is sparse(perhaps less than 64 electrodes). (See, Paul L. Nunez and RameshSrinivasan (2007) Electroencephalogram. Scholarpedia, 2(2):1348,scholarpedia.org/article/Electroencephalogram.Dipole localizationalgorithms may be useful to determine spatial emission patterns in EEG.)

Scalp potential may be expressed as a volume integral of dipole momentper unit volume over the entire brain provided P(r,t) defined generallyrather than in columnar terms. For the important case of dominantcortical sources, scalp potential may be approximated by the followingintegral over the cortical volume Θ, VS(r,t)=∫∫∫ΘG(r,r′)·P(r′,t)dΘ(r′).If the volume element dΘ(r′) is defined in terms of cortical columns,the volume integral may be reduced to an integral over the foldedcortical surface. The time-dependence of scalp potential is the weightedsum of all dipole time variations in the brain, although deep dipolevolumes typically make negligible contributions. The vector Green'sfunction G(r,r′) contains all geometric and conductive information aboutthe head volume conductor and weights the integral accordingly. Thus,each scalar component of the Green's function is essentially an inverseelectrical distance between each source component and scalp location.For the idealized case of sources in an infinite medium of constantconductivity, the electrical distance equals the geometric distance. TheGreen's function accounts for the tissue's finite spatial extent and itsinhomogeneity and anisotropy. The forward problem in EEG consists ofchoosing a head model to provide G(r,r′) and carrying out the integralfor some assumed source distribution. The inverse problem consists ofusing the recorded scalp potential distribution VS(r,t) plus someconstraints (usual assumptions) on P(r,t) to find the best fit sourcedistribution P(r,t). Since the inverse problem has no unique solution,any inverse solution depends critically on the chosen constraints, forexample, only one or two isolated sources, distributed sources confinedto the cortex, or spatial and temporal smoothness criteria.High-resolution EEG uses the experimental scalp potential VS(r,t) topredict the potential on the dura surface (the unfolded membranesurrounding the cerebral cortex) VD(r,t). This may be accomplished usinga head model Green's function G(r,r′) or by estimating the surfaceLaplacian with either spherical or 3D splines. These two approachestypically provide very similar dura potentials VD(r,t); the estimates ofdura potential distribution are unique subject to head model, electrodedensity, and noise issues.

In an EEG recording system, each electrode is connected to one input ofa differential amplifier (one amplifier per pair of electrodes); acommon system reference electrode (or synthesized reference) isconnected to the other input of each differential amplifier. Theseamplifiers amplify the voltage between the active electrode and thereference (typically 1,000-100,000 times, or 60-100 dB of voltage gain).The amplified signal is digitized via an analog-to-digital converter,after being passed through an anti-aliasing filter. Analog-to-digitalsampling typically occurs at 256-512 Hz in clinical scalp EEG; samplingrates of up to 20 kHz are used in some research applications. The EEGsignals can be captured with open source hardware such as OpenBCl, andthe signal can be processed by freely available EEG software such asEEGLAB or the Neurophysiological Biomarker Toolbox. A typical adulthuman EEG signal is about 10 μV to 100 μV in amplitude when measuredfrom the scalp and is about 10-20 mV when measured from subduralelectrodes.

Typically, a magnetic sensor with sufficient sensitivity to individualcell depolarization or small groups is a superconducting quantuminterference device (SQIUD), which requires cryogenic temperatureoperation, either at liquid nitrogen temperatures (high temperaturesuperconductors, HTS) or at liquid helium temperatures (low temperaturesuperconductors, LTS). However, current research shows possiblefeasibility of room temperature superconductors (20 C). Magnetic sensinghas an advantage, due to the dipole nature of sources, of having betterpotential volumetic localization; however, due to this addedinformation, complexity of signal analysis is increased.

In general, the electromagnetic signals detected represent actionpotentials, an automatic response of a nerve cell to depolarizationbeyond a threshold, which briefly opens conduction channels. The cellshave ion pumps which seek to maintain a depolarized state. Oncetriggered, the action potential propagates along the membrane intwo-dimensions, causing a brief high level of depolarizing ion flow.There is a quiescent period after depolarization that generally preventsoscillation within a single cell. Since the exon extends from the bodyof the neuron, the action potential will typically proceed along thelength of the axon, which terminates in a synapse with another cell.While direct electrical connections between cells occur, often the axonreleases a neurotransmitter compound into the synapse, which causes adepolarization or hyperpolarization of the target cell. Indeed, theresult may also be release of a hormone or peptide, which may have alocal or more distant effect.

The electrical fields detectable externally tend to not include signalswhich low frequency signals, such as static levels of polarization, orcumulative depolarizing or hyperpolarizing effects between actionpotentials. In myelinated tracts, the current flows at the segments tendto be small, and therefore the signals from individual cells are small.Therefore, the largest signal components are from the synapses and cellbodies. In the cerebrum and cerebellum, these structures are mainly inthe cortex, which is largely near the skull, makingelectroencephalography useful, since it provides spatial discriminationbased on electrode location. However, deep signals are attenuated, andpoorly localized. Magnetoencephalography detects dipoles, which derivefrom current flow, rather than voltage changes. In the case of aradially or spherically symmetric current flow within a short distance,the dipoles will tend to cancel, while net current flows long axons willreinforce. Therefore, an electroencephalogram reads a different signalthan a magnetoencephalogram.

EEG-based studies of emotional specificity at the single-electrode leveldemonstrated that asymmetric activity at the frontal site, especially inthe alpha (8-12 Hz) band, is associated with emotion. Voluntary facialexpressions of smiles of enjoyment produce higher left frontalactivation. Decreased left frontal activity is observed during thevoluntary facial expressions of fear. In addition to alpha bandactivity, theta band power at the frontal midline (Fm) has also beenfound to relate to emotional states. Pleasant (as opposed to unpleasant)emotions are associated with an increase in frontal midline theta power.Many studies have sought to utilize pattern classification, such asneural networks, statistical classifiers, clustering algorithms, etc.,to differentiate between various emotional states reflected in EEG.

EEG-based studies of emotional specificity at the single-electrode leveldemonstrated that asymmetric activity at the frontal site, especially inthe alpha (8-12 Hz) band, is associated with emotion. Ekman and Davidsonfound that voluntary facial expressions of smiles of enjoyment producedhigher left frontal activation (Ekman P, Davidson R J (1993) VoluntarySmiling Changes Regional Brain Activity. Psychol Sci 4: 342-345).Another study by Coan et al. found decreased left frontal activityduring the voluntary facial expressions of fear (Coan J A, Allen J J,Harmon-Jones E (2001) Voluntary facial expression and hemisphericasymmetry over the frontal cortex. Psychophysiology 38: 912-925). Inaddition to alpha band activity, theta band power at the frontal midline(Fm) has also been found to relate to emotional states. Sammler andcolleagues, for example, showed that pleasant (as opposed to unpleasant)emotion is associated with an increase in frontal midline theta power(Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) Music and emotion:Electrophysiological correlates of the processing of pleasant andunpleasant music. Psychophysiology 44: 293-304). To further demonstratewhether these emotion-specific EEG characteristics are strong enough todifferentiate between various emotional states, some studies haveutilized a pattern classification analysis approach. See, for example:

-   Dan N, Xiao-Wei W, Li-Chen S, Bao-Liang L. EEG-based emotion    recognition during watching movies; 2011 Apr. 27, 2011-May 1, 2011:    667-670;-   Lin Y P, Wang C H, Jung T P, Wu T L, Jeng S K, et al. (2010)    EEG-Based Emotion Recognition in Music Listening. Ieee T Bio Med Eng    57: 1798-1806;-   Murugappan M, Nagarajan R, Yaacob S (2010) Classification of human    emotion from EEG using discrete wavelet transform. J Biomed Sci Eng    3: 390-396;-   Murugappan M, Nagarajan R, Yaacob S (2011) Combining Spatial    Filtering and Wavelet Transform for Classifying Human Emotions Using    EEG Signals. J Med. Bio.Eng. 31: 45-51.

Detecting different emotional states by EEG may be more appropriateusing EEG-based functional connectivity. There are various ways toestimate EEG-based functional brain connectivity: correlation, coherenceand phase synchronization indices between each pair of EEG electrodeshad been used. The assumption is that a higher correlation map indicatesa stronger relationship between two signals. (Brazier M A, Casby J U(1952) Cross-correlation and autocorrelation studies ofelectroencephalographic potentials. Electroen clin neuro 4: 201-211).Coherence gives information similar to correlation, but also includesthe covaration between two signals as a function of frequency. (CanteroJ L, Atienza M, Salas R M, Gomez C M (1999) Alpha EEG coherence indifferent brain states: an electrophysiological index of the arousallevel in human subjects. Neurosci lett 271: 167-70.) The assumption isthat higher coherence indicates a stronger relationship between twosignals. (Guevara M A, Corsi-Cabrera M (1996) EEG coherence or EEGcorrelation? Int J Psychophysiology 23: 145-153; Cantero J L, Atienza M,Salas R M, Gomez C M (1999) Alpha EEG coherence in different brainstates: an electrophysiological index of the arousal level in humansubjects. Neurosci lett 271: 167-70; Adler G, Brassen S, Jajcevic A(2003) EEG coherence in Alzheimer's dementia. J Neural Transm 110:1051-1058; Deeny S P, Hillman C H, Janelle C M, Hatfield B D (2003)Cortico-cortical communication and superior performance in skilledmarksmen: An EEG coherence analysis. J Sport Exercise Psy 25: 188-204.)Phase synchronization among the neuronal groups estimated based on thephase difference between two signals is another way to estimate theEEG-based functional connectivity among brain areas. It is. (FranaszczukP J, Bergey G K (1999) An autoregressive method for the measurement ofsynchronization of interictal and ictal EEG signals. Biol Cybem 81:3-9.)

A number of groups have examined emotional specificity using EEG-basedfunctional brain connectivity. For example, Shin and Park showed that,when emotional states become more negative at high room temperatures,correlation coefficients between the channels in temporal and occipitalsites increase (Shin J-H, Park D-H. (2011) Analysis for Characteristicsof Electroencephalogram (EEG) and Influence of Environmental FactorsAccording to Emotional Changes. In Lee G, Howard D, Ślęzak D, editors.Convergence and Hybrid Information Technology. Springer BerlinHeidelberg, 488-500.) Hinrichs and Machleidt demonstrated that coherencedecreases in the alpha band during sadness, compared to happiness(Hinrichs H, Machleidt W (1992) Basic emotions reflected inEEG-coherences. Int J Psychophysiol 13: 225-232). Miskovic and Schmidtfound that EEG coherence between the prefrontal cortex and the posteriorcortex increased while viewing highly emotionally arousing (i.e.,threatening) images, compared to viewing neutral images (Miskovic V,Schmidt L A (2010) Cross-regional cortical synchronization duringaffective image viewing. Brain Res 1362: 102-111). Costa and colleaguesapplied the synchronization index to detect interaction in differentbrain sites under different emotional states (Costa T, Rognoni E, GalatiD (2006) EEG phase synchronization during emotional response to positiveand negative film stimuli. Neurosci Lett 406: 159-164). Costa's resultsshowed an overall increase in the synchronization index among frontalchannels during emotional stimulation, particularly during negativeemotion (i.e., sadness). Furthermore, phase synchronization patternswere found to differ between positive and negative emotions. Costa alsofound that sadness was more synchronized than happiness at eachfrequency band and was associated with a wider synchronization bothbetween the right and left frontal sites and within the left hemisphere.In contrast, happiness was associated with a wider synchronizationbetween the frontal and occipital sites.

Different connectivity indices are sensitive to differentcharacteristics of EEG signals. Correlation is sensitive to phase andpolarity, but is independent of amplitudes. Changes in both amplitudeand phase lead to a change in coherence (Guevara M A, Corsi-Cabrera M(1996) EEG coherence or EEG correlation? Int J Psychophysiol 23:145-153). The phase synchronization index is only sensitive to a changein phase (Lachaux J P, Rodriguez E, Martinerie J, Varela F J (1999)Measuring phase synchrony in brain signals. Hum Brain Mapp 8: 194-208).

A number of studies have tied to classify emotional states by means ofrecording and statistically analyzing EEG signals from the centralnervous systems. See for example:

-   Lin Y P, Wang C H, Jung T P, Wu T L, Jeng S K, et al. (2010)    EEG-Based Emotion Recognition in Music Listening. IEEE T Bio Med Eng    57:1798-1806-   Murugappan M, Nagarajan R, Yaacob S (2010) Classification of human    emotion from EEG using discrete wavelet transform. J Biomed Sci Eng    3: 390-396.-   Murugappan M, Nagarajan R, Yaacob S (2011) Combining Spatial    Filtering and Wavelet Transform for Classifying Human Emotions Using    EEG Signals. J Med. Bio.Eng. 31: 45-51.-   Berkman E, Wong D K, Guimaraes M P, Uy E T, Gross J J, et al. (2004)    Brain wave recognition of emotions in EEG. Psychophysiology 41:    S71-S71.-   Chanel G, Kronegg J, Grandjean D, Pun T (2006) Emotion assessment    Arousal evaluation using EEGs and peripheral physiological signals.    Multimedia Content Representation, Classification and Security 4105:    530-537.-   Hagiwara KlaM (2003) A Feeling Estimation System Using a Simple    Electroencephalograph. IEEE International Conference on Systems, Man    and Cybemetics. 4204-4209.-   You-Yun Lee and Shulan Hsieh studied different emotional states by    means of EEG-based functional connectivity patterns. They used    emotional film clips to elicit three different emotional states.

The dimensional theory of emotion, which asserts that there are neutral,positive, and negative emotional states, may be used to classifyemotional states, because numerous studies have suggested that theresponses of the central nervous system correlate with emotional valenceand arousal. As suggested by Mauss and Robins (2009), “measures ofemotional responding appear to be structured along dimensions (e.g.,valence, arousal) rather than discrete emotional states (e.g., sadness,fear, anger)”. See for example:

-   Davidson R J (1993) Cerebral Asymmetry and Emotion—Conceptual and    Methodological Conundrums. Cognition Emotion 7:115-138;-   Jones N A, Fox N A (1992) Electroencephalogram asymmetry during    emotionally evocative films and its relation to positive and    negative affectivity. Brain Cogn 20: 280-299;-   Schmidt L A, Trainor L J (2001) Frontal brain electrical activity    (EEG) distinguishes valence and intensity of musical emotions.    Cognition Emotion 15: 487-500;-   Tomarken A J, Davidson R J, Henriques J B (1990) Resting frontal    brain asymmetry predicts affective responses to films. J Pers Soc    Psychol 59: 791-801.)

EEG-based functional connectivity change was found to be significantlydifferent among emotional states of neutral, positive, or negative. LeeY-Y, Hsieh S (2014) Classifying Different Emotional States by Means ofEEG-Based Functional Connectivity Patterns. PLoS ONE 9(4): e95415.doi.org/10.1371/journal.pone.0095415. A connectivity pattern may bedetected by pattern classification analysis using Quadratic DiscriminantAnalysis. The results indicated that the classification rate was betterthan chance. The authors found the following correlations:

Theta Band.

Compared to neutral emotions, a significantly lower correlation at thefrontal site and higher correlations at the temporal and occipital siteswere found when watching negative films. No differences between anegative state and a positive state were found in the theta band. Asignificantly lower correlation was found in a positive state than in aneutral state at the frontal and parietal sites. A positive state showedhigher correlations than a neutral state mainly at the temporal,parietal and occipital sites.

Alpha band.

A significantly higher correlation was found in a neutral state only inthe case of F7-P7 activity. A negative state showed a significantlyhigher correlation than a positive state, especially at the parietal andoccipital sites. A neutral state showed a lower correlation than apositive state mainly at the right temporal site.

Beta band.

No significant difference in correlation was observed among emotionalstates in the beta band.

Gamma band.

No significant difference in correlation was observed among emotionalstates in the gamma band.

They concluded that estimating EEG-based functional connectivityprovides a useful tool for studying the relationship between brainactivity and emotional states.

Emotions affect learning. Intelligent Tutoring Systems (ITS) learnermodel initially composed of a cognitive module was extended to include apsychological module and an emotional module. Alicia Heraz et al.introduced an emomental agent. It interacts with an ITS to communicatethe emotional state of the learner based upon his mental state. Themental state was obtained from the learner's brainwaves. The agentlearns to predict the learner's emotions by using ML techniques. (AliciaHeraz, Ryad Razaki; Claude Frasson, “Using machine learning to predictlearner emotional state from brainwaves” Advanced Learning Technologies,2007. ICA LT 2007. Seventh IEEE International Conference on AdvancedLearning Technologies (ICALT 2007)) See also:

-   Ella T. Mampusti, Jose S. Ng, Jarren James I. Quinto, Grizelda L.    Teng, Merlin Teodosia C. Suarez, Rhia S. Trogo, “Measuring Academic    Affective States of Students via Brainwave Signals”, Knowledge and    Systems Engineering (KSE) 2011 Third International Conference on,    pp. 226-231, 2011-   Judith J. Azcarraga, John Francis Ibanez Jr., Ianne Robert Lim,    Nestor Lumanas Jr., “Use of Personality Profile in Predicting    Academic Emotion Based on Brainwaves Signals and Mouse Behavior”,    Knowledge and Systems Engineering (KSE) 2011 Third International    Conference on, pp. 239-244, 2011.-   Yi-Hung Liu, Chien-Te Wu, Yung-Hwa Kao, Ya-Ting Chen, “Single-trial    EEG-based emotion recognition using kernel Eigen-emotion pattern and    adaptive support vector machine”, Engineering in Medicine and    Biology Society (EMBC) 2013 35th Annual International Conference of    the IEEE, pp. 4306-4309, 2013, ISSN 1557-170X.-   Thong Tri Vo, Nam Phuong Nguyen, Toi Vo Van, IFMBE Proceedings, vol.    63, pp. 621, 2018, ISSN 1680-0737, ISBN 978-981-10-4360-4.-   Adrian Rodriguez Aguiñaga, Miguel Angel Lopez Ramirez, Lecture Notes    in Computer Science, vol. 9456, pp. 177, 2015, ISSN 0302-9743, ISBN    978-3-319-26507-0.-   Judith Azcarraga, Merlin Teodosia Suarez, “Recognizing Student    Emotions using Brainwaves and Mouse Behavior Data”, International    Journal of Distance Education Technologies, vol. 11, pp. 1, 2013,    ISSN 1539-3100.-   Tri Thong Vo, Phuong Nam Nguyen, Van Toi Vo, IFMBE Proceedings, vol.    61, pp. 67, 2017, ISSN 1680-0737, ISBN 978-981-10-4219-5.-   Alicia Heraz, Claude Frasson, Lecture Notes in Computer Science,    vol. 5535, pp. 367, 2009, ISSN 0302-9743, ISBN 978-3-642-02246-3.-   Hamwira Yaacob, Wahab Abdul, Norhaslinda Kamaruddin, “Classification    of EEG signals using MLP based on categorical and dimensional    perceptions of emotions”, Information and Communication Technology    for the Muslim World (ICT4M) 2013 5th International Conference on,    pp. 1-6, 2013.-   Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang    Jeng, Jeng-Ren Duann, Jyh-Homg Chen, “EEG-Based Emotion Recognition    in Music Listening”, Biomedical Engineering IEEE Transactions on,    vol. 57, pp. 1798-1806, 2010, ISSN 0018-9294.-   Yi-Hung Liu, Wei-Teng Cheng, Yu-Tsung Hsiao, Chien-Te Wu, Mu-Der    Jeng, “EEG-based emotion recognition based on kernel Fishers    discriminant analysis and spectral powers”, Systems Man and    Cybemeics (SMC) 2014 IEEE International Conference on, pp.    2221-2225, 2014.

Using EEG to assess the emotional state has numerous practicalapplications. One of the first such applications was the development ofa travel guide based on emotions by measuring brainwaves by theSingapore tourism group. “By studying the brainwaves of a family onvacation, the researchers drew up the Singapore Emotion Travel Guide,which advises future visitors of the emotions they can expect toexperience at different attractions.”(www.lonelyplanetcom.news/2017/04/12/singapore-emotion-travel-guide)Joel Pearson at University of New South Wales and his group developedthe protocol of measuring brainwaves of travelers using EEG and decodingspecific emotional states.

Another recently released application pertains to virtual reality (VR)technology. On Sep. 18, 2017 Looxid Labs launched a technology thatharnesses EEG from a subject waring a VR headset Looxid Labs intentionis to factor in brainwaves into VR applications in order to accuratelyinfer emotions. Other products such as MindMaze and even Samsung havetied creating similar applications through facial muscles recognition.(scottamyx.com/2017/10/13/looxid-labs-vr-brain-waves-human-emotions/).According to its website (looxidlabs.cormdevice-2/), the Looxid LabsDevelopment Kit provides a VR headset embedded with miniaturized eye andbrain sensors. It uses 6 EEG channels: Fp1, Fp2, AF7, AF8, AF3, AF4 ininternational 10-20 system.

To assess a user's state of mind, a computer may be used to analyze theEEG signals produced by the brain of the user. However, the emotionalstates of a brain are complex, and the brainwaves associated withspecific emotions seem to change over time. Wei-Long Zheng at ShanghaiJiao Tong University used machine learning (ML) to identify theemotional brain states and to repeat it reliably. The ML algorithm founda set of patterns that clearly distinguished positive, negative, andneutral emotions that worked for different subjects and for the samesubjects over time with an accuracy of about 80 percent (See Wei-LongZheng, Jia-Yi Zhu, Bao-Liang Lu, Identifying Stable Patterns over Timefor Emotion Recognition from EEG, arDdv.org/abs/1601.02197; see also HowOne Intelligent Machine Leaned to Recognize Human Emotions, MITTechnology Review, Jan. 23, 2016.)

MEG

Magnetoencephalography (MEG) is a functional neuroimaging technique formapping brain activity by recording magnetic fields produced byelectrical currents occurring naturally in the brain, using verysensitive magnetometers. Arrays of SQUIDs (superconducting quantuminterference devices) are currently the most common magnetometer, whilethe SERF (spin exchange relaxation-free) magnetometer is beinginvestigated (Himalinen, Matti; Hadi, Riitta; Ilmoniemi, Risto J;Knuutila, Jukka; Lounasmaa, Olli V. (1993).“Magnetoencephalography-theory, instrumentation, and applications tononinvasive studies of the working human brain”. Reviews of ModernPhysics. 65 (2): 413-497. ISSN 0034-6861.doi:10.1103/RevModPhys.65.413.) It is known that “neuronal activitycauses local changes in cerebral blood flow, blood volume, and bloodoxygenation” (Dynamic magnetic resonance imaging of human brain activityduring primary sensory stimulation. K. K. Kwong, J. W. Belliveau, D. A.Chesler, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy,B. E. Hoppel, M. S. Cohen, and R. Turner). Using “a 122-channel D.C.SQUID magnetometer with a helmet-shaped detector array covering thesubjects head” it has been shown that the “system allows simultaneousrecording of magnetic activity all over the head.” (122-channel squidinstrument for investigating the magnetic signals from the human brain.)A. I. Ahonen, M. S. Hämäläinen, M. J. Kajola, J. E. T. Knuutila, P. P.Laine, O. V. Lounasmaa, L. T. Parkkonen, J. T. Simola, and C. D. TeschePhysica Scripta, Volume 1993, T49A).

In some cases, magnetic fields cancel, and thus the detectableelectrical activity may fundamentally differ from the detectableelectrical activity obtained via EEG. However, the main types of brainrhythms are detectable by both methods.

See: U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 5,059,814; 5,118,606;5,136,687; 5,224,203; 5,303,705; 5,325,862; 5,461,699; 5,522,863;5,640,493; 5,715,821; 5,719,561; 5,722,418; 5,730,146; 5,736,543;5,737,485; 5,747,492; 5,791,342; 5,816,247; 6,497,658; 6,510,340;6,654,729; 6,893,407; 6,950,697; 8,135,957; 8,620,206; 8,644,754;9,118,775; 9,179,875; 9,642,552; 20030018278; 20030171689; 20060293578;20070156457; 20070259323; 20080015458; 20080154148; 20080229408;20100010365; 20100076334; 20100090835; 20120046531; 20120052905;20130041281; 20150081299; 20150262016. See EP1304073A2; EP1304073A3;WO2000025668A1; and WO2001087153A1.

MEGs seek to detect the magnetic dipole emission from an electricaldischarge in cells, e.g., neural action potentials. Typical sensors forMEGs are superconducting quantum interference devices (SQUIDs). Thesecurrently require cooling to liquid nitrogen or liquid heliumtemperatures. However, the development of room temperature, or near roomtemperature superconductors, and miniature cryocoolers, may permit fielddeployments and portable or mobile detectors. Because MEGs are lessinfluenced by medium conductivity and dielectric properties, and becausethey inherently detect the magnetic field vector, MEG technology permitsvolumetric mapping of brain activity and distinction of complementaryactivity that might suppress detectable EEG signals. MEG technology alsosupports vector mapping of fields, since magnetic emitters areinherently dipoles, and therefore a larger amount of information isinherently available.

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 4,862,359; 5,027,817;5,198,977; 5,230,346; 5,269,315; 5,309,923; 5,325,862; 5,331,970;5,546,943; 5,568,816; 5,662,109; 5,724,987; 5,797,853; 5,840,040;5,845,639; 6,042,548; 6,080,164; 6,088,611; 6,097,980; 6,144,872;6,161,031; 6,171,239; 6,240,308; 6,241,686; 6,280,393; 6,309,361;6,319,205; 6,322,515; 6,356,781; 6,370,414; 6,377,833; 6,385,479;6,390,979; 6,402,689; 6,419,629; 6,466,816; 6,490,472; 6,526,297;6,527,715; 6,530,884; 6,547,746; 6,551,243; 6,553,252; 6,622,036;6,644,976; 6,648,880; 6,663,571; 6,684,098; 6,697,660; 6,728,564;6,740,032; 6,743,167; 6,773,400; 6,907,280; 6,947,790; 6,950,698;6,963,770; 6,963,771; 6,996,261; 7,010,340; 7,011,814; 7,022,083;7,092,748; 7,104,947; 7,105,824; 7,120,486; 7,130,673; 7,171,252;7,177,675; 7,231,245; 7,254,500; 7,283,861; 7,286,871; 7,338,455;7,346,395; 7,378,056; 7,461,045; 7,489,964; 7,490,085; 7,499,745;7,510,699; 7,539,528; 7,547,284; 7,565,193; 7,567,693; 7,577,472;7,613,502; 7,627,370; 7,647,098; 7,653,433; 7,697,979; 7,729,755;7,754,190; 7,756,568; 7,766,827; 7,769,431; 7,778,692; 7,787,937;7,787,946; 7,794,403; 7,831,305; 7,840,250; 7,856,264; 7,860,552;7,899,524; 7,904,139; 7,904,144; 7,933,645; 7,962,204; 7,983,740;7,986,991; 8,000,773; 8,000,793; 8,002,553; 8,014,847; 8,036,434;8,065,360; 8,069,125; 8,086,296; 8,121,694; 8,190,248; 8,190,264;8,197,437; 8,224,433; 8,233,682; 8,233,965; 8,236,038; 8,262,714;8,280,514; 8,295,914; 8,306,607; 8,306,610; 8,313,441; 8,326,433;8,337,404; 8,346,331; 8,346,342; 8,356,004; 8,358,818; 8,364,271;8,380,289; 8,380,290; 8,380,314; 8,391,942; 8,391,956; 8,423,125;8,425,583; 8,429,225; 8,445,851; 8,457,746; 8,467,878; 8,473,024;8,498,708; 8,509,879; 8,527,035; 8,532,756; 8,538,513; 8,543,189;8,554,325; 8,562,951; 8,571,629; 8,586,932; 8,591,419; 8,606,349;8,606,356; 8,615,479; 8,626,264; 8,626,301; 8,632,750; 8,644,910;8,655,817; 8,657,756; 8,666,478; 8,679,009; 8,684,926; 8,690,748;8,696,722; 8,706,205; 8,706,241; 8,706,518; 8,712,512; 8,717,430;8,725,669; 8,738,395; 8,761,869; 8,761,889; 8,768,022; 8,805,516;8,814,923; 8,831,731; 8,834,546; 8,838,227; 8,849,392; 8,849,632;8,852,103; 8,855,773; 8,858,440; 8,868,174; 8,888,702; 8,915,741;8,918,162; 8,938,289; 8,938,290; 8,951,189; 8,951,192; 8,956,277;8,965,513; 8,977,362; 8,989,836; 8,998,828; 9,005,126; 9,020,576;9,022,936; 9,026,217; 9,026,218; 9,028,412; 9,033,884; 9,037,224;9,042,201; 9,050,470; 9,067,052; 9,072,905; 9,084,896; 9,089,400;9,089,683; 9,092,556; 9,095,266; 9,101,276; 9,107,595; 9,116,835;9,133,024; 9,144,392; 9,149,255; 9,155,521; 9,167,970; 9,167,976;9,167,977; 9,167,978; 9,171,366; 9,173,609; 9,179,850; 9,179,854;9,179,858; 9,179,875; 9,192,300; 9,198,637; 9,198,707; 9,204,835;9,211,077; 9,211,212; 9,213,074; 9,242,067; 9,247,890; 9,247,924;9,248,288; 9,254,097; 9,254,383; 9,268,014; 9,268,015; 9,271,651;9,271,674; 9,282,930; 9,289,143; 9,302,110; 9,308,372; 9,320,449;9,322,895; 9,326,742; 9,332,939; 9,336,611; 9,339,227; 9,357,941;9,367,131; 9,370,309; 9,375,145; 9,375,564; 9,387,320; 9,395,425;9,402,558; 9,403,038; 9,414,029; 9,436,989; 9,440,064; 9,463,327;9,470,728; 9,471,978; 9,474,852; 9,486,632; 9,492,313; 9,560,967;9,579,048; 9,592,409; 9,597,493; 9,597,494; 9,615,789; 9,616,166;9,655,573; 9,655,669; 9,662,049; 9,662,492; 9,669,185; 9,675,292;9,682,232; 9,687,187; 9,707,396; 9,713,433; 9,713,444; 20010020127;20010021800; 20010051774; 20020005784; 20020016552; 20020017994;20020042563; 20020058867; 20020099273; 20020099295; 20020103428;20020103429; 20020128638; 20030001098; 20030009096; 20030013981;20030032870; 20030040660; 20030068605; 20030074032; 20030093004;20030093005; 20030120140; 20030128801; 20030135128; 20030153818;20030163027; 20030163028; 20030181821; 20030187359; 20030204135;20030225335; 20030236458; 20040030585; 20040059241; 20040072133;20040077960; 20040092809; 20040096395; 20040097802; 20040116798;20040122787; 20040122790; 20040144925; 20040204656; 20050004489;20050007091; 20050027284; 20050033122; 20050033154; 20050033379;20050079474; 20050079636; 20050106713; 20050107654; 20050119547;20050131311; 20050136002; 20050159670; 20050159671; 20050182456;20050192514; 20050222639; 20050283053; 20060004422; 20060015034;20060018525; 20060036152; 20060036153; 20060051814; 20060052706;20060058683; 20060074290; 20060074298; 20060078183; 20060084858;20060100526; 20060111644; 20060116556; 20060122481; 20060129324;20060173510; 20060189866; 20060241373; 20060241382; 20070005115;20070007454; 20070008172; 20070015985; 20070032737; 20070055145;20070100251; 20070138886; 20070179534; 20070184507; 20070191704;20070191727; 20070203401; 20070239059; 20070250138; 20070255135;20070293760; 20070299370; 20080001600; 20080021332; 20080021340;20080033297; 20080039698; 20080039737; 20080042067; 20080058664;20080091118; 20080097197; 20080123927; 20080125669; 20080128626;20080154126; 20080167571; 20080221441; 20080230702; 20080230705;20080249430; 20080255949; 20080275340; 20080306365; 20080311549;20090012387; 20090018407; 20090018431; 20090018462; 20090024050;20090048507; 20090054788; 20090054800; 20090054958; 20090062676;20090078875; 20090082829; 20090099627; 20090112117; 20090112273;20090112277; 20090112278; 20090112279; 20090112280; 20090118622;20090131995; 20090137923; 20090156907; 20090156955; 20090157323;20090157481; 20090157482; 20090157625; 20090157662; 20090157751;20090157813; 20090163777; 20090164131; 20090164132; 20090171164;20090172540; 20090177050; 20090179642; 20090191131; 20090209845;20090216091; 20090220429; 20090221928; 20090221930; 20090246138;20090264785; 20090267758; 20090270694; 20090287271; 20090287272;20090287273; 20090287274; 20090287467; 20090292180; 20090292713;20090292724; 20090299169; 20090304582; 20090306531; 20090306534;20090318773; 20090318794; 20100021378; 20100030073; 20100036233;20100036453; 20100041962; 20100042011; 20100049276; 20100069739;20100069777; 20100076274; 20100082506; 20100087719; 20100094154;20100094155; 20100099975; 20100106043; 20100113959; 20100114193;20100114237; 20100130869; 20100143256; 20100163027; 20100163028;20100163035; 20100168525; 20100168529; 20100168602; 20100189318;20100191095; 20100191124; 20100204748; 20100248275; 20100249573;20100261993; 20100298735; 20100324441; 20110004115; 20110004412;20110009777; 20110015515; 20110015539; 20110028859; 20110034821;20110046491; 20110054345; 20110054562; 20110077503; 20110092800;20110092882; 20110112394; 20110112426; 20110119212; 20110125048;20110125238; 20110129129; 20110144521; 20110160543; 20110160607;20110160608; 20110161011; 20110178359; 20110178441; 20110178442;20110207988; 20110208094; 20110213200; 20110218405; 20110230738;20110257517; 20110263962; 20110263968; 20110270074; 20110270914;20110275927; 20110295143; 20110295166; 20110301448; 20110306845;20110306846; 20110307029; 20110313268; 20110313487; 20120004561;20120021394; 20120022343; 20120022884; 20120035765; 20120046531;20120046971; 20120053449; 20120053483; 20120078327; 20120083700;20120108998; 20120130228; 20120130229; 20120149042; 20120150545;20120163689; 20120165899; 20120165904; 20120197163; 20120215114;20120219507; 20120226091; 20120226185; 20120232327; 20120232433;20120245493; 20120253219; 20120253434; 20120265267; 20120271148;20120271151; 20120271376; 20120283502; 20120283604; 20120296241;20120296253; 20120296569; 20120302867; 20120310107; 20120310298;20120316793; 20130012804; 20130063434; 20130066350; 20130066391;20130066394; 20130072780; 20130079621; 20130085678; 20130096441;20130096454; 20130102897; 20130109996; 20130110616; 20130116561;20130131755; 20130138177; 20130172716; 20130178693; 20130184728;20130188854; 20130204085; 20130211238; 20130226261; 20130231580;20130238063; 20130245422; 20130245424; 20130245486; 20130261506;20130274586; 20130281879; 20130281890; 20130289386; 20130304153;20140000630; 20140005518; 20140031703; 20140057232; 20140058241;20140058292; 20140066763; 20140081115; 20140088377; 20140094719;20140094720; 20140111335; 20140114207; 20140119621; 20140128763;20140135642; 20140148657; 20140151563; 20140155952; 20140163328;20140163368; 20140163409; 20140171749; 20140171757; 20140171819;20140180088; 20140180092; 20140180093; 20140180094; 20140180095;20140180096; 20140180097; 20140180099; 20140180100; 20140180112;20140180113; 20140180176; 20140180177; 20140193336; 20140194726;20140200414; 20140211593; 20140228649; 20140228702; 20140243614;20140243652; 20140243714; 20140249360; 20140249445; 20140257073;20140270438; 20140275807; 20140275851; 20140275891; 20140276013;20140276014; 20140276187; 20140276702; 20140279746; 20140296646;20140296655; 20140303425; 20140303486; 20140316248; 20140323849;20140330268; 20140330394; 20140335489; 20140336489; 20140340084;20140343397; 20140357962; 20140364721; 20140371573; 20140378830;20140378941; 20150011866; 20150011877; 20150018665; 20150018905;20150024356; 20150025408; 20150025422; 20150025610; 20150029087;20150033245; 20150033258; 20150033259; 20150033262; 20150033266;20150035959; 20150038812; 20150038822; 20150038869; 20150039066;20150073237; 20150080753; 20150088120; 20150119658; 20150119689;20150119698; 20150140528; 20150141529; 20150141773; 20150150473;20150151142; 20150157266; 20150165239; 20150174418; 20150182417;20150196800; 20150201879; 20150208994; 20150219732; 20150223721;20150227702; 20150230744; 20150246238; 20150247921; 20150257700;20150290420; 20150297106; 20150297893; 20150305799; 20150305800;20150305801; 20150306340; 20150313540; 20150317796; 20150320591;20150327813; 20150335281; 20150335294; 20150339363; 20150343242;20150359431; 20150360039; 20160001065; 20160001096; 20160001098;20160008620; 20160008632; 20160015289; 20160022165; 20160022167;20160022168; 20160022206; 20160027342; 20160029946; 20160029965;20160038049; 20160038559; 20160048659; 20160051161; 20160051162;20160058354; 20160058392; 20160066828; 20160066838; 20160081613;20160100769; 20160120480; 20160128864; 20160143541; 20160143574;20160151018; 20160151628; 20160157828; 20160158553; 20160166219;20160184599; 20160196393; 20160199241; 20160203597; 20160206380;20160206871; 20160206877; 20160213276; 20160235324; 20160235980;20160235983; 20160239966; 20160239968; 20160245670; 20160245766;20160270723; 20160278687; 20160287118; 20160287436; 20160296746;20160302720; 20160303397; 20160303402; 20160320210; 20160339243;20160341684; 20160361534; 20160366462; 20160371721; 20170021161;20170027539; 20170032098; 20170039706; 20170042474; 20170043167;20170065349; 20170079538; 20170080320; 20170085855; 20170086729;20170086763; 20170087367; 20170091418; 20170112403; 20170112427;20170112446; 20170112577; 20170147578; 20170151435; 20170160360;20170164861; 20170164862; 20170164893; 20170164894; 20170172527;20170173262; 20170185714; 20170188862; 20170188866; 20170188868;20170188869; 20170188932; 20170189691; 20170196501; and 20170202633.

-   Allen, Philip B., et al. High-temperature superconductivity.    Springer Science & Business Media, 2012;-   Fausti, Daniele, et al. “Light-induced superconductivity in a    stripe-ordered cuprate.” Science 331.6014 (2011): 189-191;-   Inoue, Mitsuteru, et al. “investigating the use of magnonic crystals    as extremely sensitive magnetic field sensors at room temperature.”    Applied Physics Letters 98.13 (2011): 132511;-   Kaiser, Stefan, et al. “Optically induced coherent transport far    above Tc in underdoped YBa2 Cu3O6+δ.” Physical Review B 89.18    (2014): 184516;-   Malik, M. A., and B. A. Malik. “High Temperature Superconductivity:    Materials, Mechanism and Applications.” Bulgarian J. Physics 41.4    (2014).-   Mankowsky, Roman, et al. “Nonlinear lattice dynamics as a basis for    enhanced superconductivity in YBa2Cu3O6. δ.” arXiv preprint    arXiv:1405.2266 (2014);-   Mcfetridge, Grant “Room temperature superconductor.” U.S. Pub. App.    No. 20020006875.-   Mitrano, Matteo, et al. “Possible light-induced superconductivity in    K3C60 at high temperature.” Nature 530.7591 (2016): 461-464;-   Mourachkine, Andrei. Room-temperature superconductivity. Cambridge    Int Science Publishing, 2004;-   Narlikar, Anant V., ed. High Temperature Superconductivity 2.    Springer Science & Business Media, 2013;-   Pickett, Warren E. “Design for a room-temperature    superconductor.” J. superconductivity and novel magnetism 19.3    (2006): 291-297;-   Sleight, Arthur W. “Room temperature superconductors.” Accounts of    chemical research 28.3 (1995): 103-108.-   Hämäläinen, Matti; Hari, Riitta; Ilmoniemi, Risto J; Knuutila,    Jukka; Lounasmaa, Olli V. (1993). “Magnetoencephalography-theory,    instrumentation, and applications to noninvasive studies of the    working human brain”. Reviews of Modern Physics. 65 (2): 413-497.    ISSN 0034-6861. doi:10.1103/RevModPhys.65.413.

EEGs and MEGs can monitor the state of consciousness. For example,states of deep sleep are associated with slower EEG oscillations oflarger amplitude. Various signal analysis methods allow for robustidentifications of distinct sleep stages, depth of anesthesia, epilepticseizures and connections to detailed cognitive events.

Positron Emission Tomography (PET) Scan

A PET scan is an imaging test that helps reveal how tissues and organsare functioning (Bailey, D. L; D. W. Townsend; P. E. Valk; M. N. Maisey(2005). Positron Emission Tomography: Basic Sciences. Secaucus, N.J.:Springer-Verlag. ISBN 1-85233-798-2.). A PET scan uses a radioactivedrug (positron-emitting tracer) to show this activity. It uses thisradiation to produce 3-D, images colored for the different activity ofthe brain. See, e.g.:

-   Jarden, Jens O., Vijay Dhawan, Alexander Potorak, Jerome B. Posner,    and David A. Rottenberg. “Positron emission tomographic measurement    of blood-to-brain and blood-to-tumor transport of 82Rb: The effect    of dexamethasone and whole-brain radiation therapy.” Annals of    neurology 18, no. 6 (1985): 636-646.-   Dhawan, V. I. J. A. Y., A. Potorak, J. R. Moeller, J. O.    Jarden, S. C. Strother, H. Thaler, and D. A. Rottenberg. “Positron    emission tomographic measurement of blood-to-brain and    blood-to-tumour transport of 82Rb. I: Error analysis and computer    simulations.” Physics in medicine and biology 34, no. 12 (1989):    1773.

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 4,977,505; 5,331,970;5,568,816; 5,724,987; 5,825,830; 5,840,040; 5,845,639; 6,053,739;6,132,724; 6,161,031; 6,226,418; 6,240,308; 6,266,453; 6,364,845;6,408,107; 6,490,472; 6,547,746; 6,615,158; 6,633,686; 6,644,976;6,728,424; 6,775,405; 6,885,886; 6,947,790; 6,996,549; 7,117,026;7,127,100; 7,150,717; 7,254,500; 7,309,315; 7,355,597; 7,367,807;7,383,237; 7,483,747; 7,583,857; 7,627,370; 7,647,098; 7,678,047;7,738,683; 7,778,490; 7,787,946; 7,876,938; 7,884,101; 7,890,155;7,901,211; 7,904,144; 7,961,922; 7,983,762; 7,986,991; 8,002,553;8,069,125; 8,090,164; 8,099,299; 8,121,361; 8,126,228; 8,126,243;8,148,417; 8,148,418; 8,150,796; 8,160,317; 8,167,826; 8,170,315;8,170,347; 8,175,359; 8,175,360; 8,175,686; 8,180,125; 8,180,148;8,185,186; 8,195,593; 8,199,982; 8,199,985; 8,233,689; 8,233,965;8,249,815; 8,303,636; 8,306,610; 8,311,747; 8,311,748; 8,311,750;8,315,812; 8,315,813; 8,315,814; 8,321,150; 8,356,004; 8,358,818;8,374,411; 8,379,947; 8,386,188; 8,388,529; 8,423,118; 8,430,816;8,463,006; 8,473,024; 8,496,594; 8,520,974; 8,523,779; 8,538,108;8,571,293; 8,574,279; 8,577,103; 8,588,486; 8,588,552; 8,594,950;8,606,356; 8,606,361; 8,606,530; 8,606,592; 8,615,479; 8,630,812;8,634,616; 8,657,756; 8,664,258; 8,675,936; 8,675,983; 8,680,119;8,690,748; 8,706,518; 8,724,871; 8,725,669; 8,734,356; 8,734,357;8,738,395; 8,754,238; 8,768,022; 8,768,431; 8,785,441; 8,787,637;8,795,175; 8,812,245; 8,812,246; 8,838,201; 8,838,227; 8,861,819;8,868,174; 8,871,797; 8,913,810; 8,915,741; 8,918,162; 8,934,685;8,938,102; 8,980,891; 8,989,836; 9,025,845; 9,034,911; 9,037,224;9,042,201; 9,053,534; 9,064,036; 9,076,212; 9,078,564; 9,081,882;9,082,169; 9,087,147; 9,095,266; 9,138,175; 9,144,392; 9,149,197;9,152,757; 9,167,974; 9,171,353; 9,171,366; 9,177,379; 9,177,416;9,179,854; 9,186,510; 9,198,612; 9,198,624; 9,204,835; 9,208,430;9,208,557; 9,211,077; 9,221,755; 9,226,672; 9,235,679; 9,256,982;9,268,902; 9,271,657; 9,273,035; 9,275,451; 9,282,930; 9,292,858;9,295,838; 9,305,376; 9,311,335; 9,320,449; 9,328,107; 9,339,200;9,339,227; 9,367,131; 9,370,309; 9,390,233; 9,396,533; 9,401,021;9,402,558; 9,412,076; 9,418,368; 9,434,692; 9,436,989; 9,449,147;9,451,303; 9,471,978; 9,472,000; 9,483,613; 9,495,684; 9,556,149;9,558,558; 9,560,967; 9,563,950; 9,567,327; 9,582,152; 9,585,723;9,600,138; 9,600,778; 9,604,056; 9,607,377; 9,613,186; 9,652,871;9,662,083; 9,697,330; 9,706,925; 9,717,461; 9,729,252; 9,732,039;9,734,589; 9,734,601; 9,734,632; 9,740,710; 9,740,946; 9,741,114;9,743,835; RE45336; RE45337; 20020032375; 20020183607; 20030013981;20030028348; 20030031357; 20030032870; 20030068605; 20030128801;20030233039; 20030233250; 20030234781; 20040049124; 20040072133;20040116798; 20040151368; 20040184024; 20050007091; 20050065412;20050080124; 20050096311; 20050118286; 20050144042; 20050215889;20050244045; 20060015153; 20060074290; 20060084858; 20060129324;20060188134; 20070019846; 20070032737; 20070036402; 20070072857;20070078134; 20070081712; 20070100251; 20070127793; 20070280508;20080021340; 20080069446; 20080123927; 20080167571; 20080219917;20080221441; 20080241804; 20080247618; 20080249430; 20080279436;20080281238; 20080286453; 20080287774; 20080287821; 20080298653;20080298659; 20080310697; 20080317317; 20090018407; 20090024050;20090036781; 20090048507; 20090054800; 20090074279; 20090099783;20090143654; 20090148019; 20090156907; 20090156955; 20090157323;20090157481; 20090157482; 20090157625; 20090157660; 20090157751;20090157813; 20090163777; 20090164131; 20090164132; 20090164302;20090164401; 20090164403; 20090164458; 20090164503; 20090164549;20090171164; 20090172540; 20090221904; 20090246138; 20090264785;20090267758; 20090270694; 20090271011; 20090271120; 20090271122;20090271347; 20090290772; 20090292180; 20090292478; 20090292551;20090299435; 20090312595; 20090312668; 20090316968; 20090316969;20090318773; 20100004762; 20100010316; 20100010363; 20100014730;20100014732; 20100015583; 20100017001; 20100022820; 20100030089;20100036233; 20100041958; 20100041962; 20100041964; 20100042011;20100042578; 20100063368; 20100069724; 20100069777; 20100076249;20100080432; 20100081860; 20100081861; 20100094155; 20100100036;20100125561; 20100130811; 20100130878; 20100135556; 20100142774;20100163027; 20100163028; 20100163035; 20100168525; 20100168529;20100168602; 20100172567; 20100179415; 20100189318; 20100191124;20100219820; 20100241449; 20100249573; 20100260402; 20100268057;20100268108; 20100274577; 20100274578; 20100280332; 20100293002;20100305962; 20100305963; 20100312579; 20100322488; 20100322497;20110028825; 20110035231; 20110038850; 20110046451; 20110077503;20110125048; 20110152729; 20110160543; 20110229005; 20110230755;20110263962; 20110293193; 20120035765; 20120041318; 20120041319;20120041320; 20120041321; 20120041322; 20120041323; 20120041324;20120041498; 20120041735; 20120041739; 20120053919; 20120053921;20120059246; 20120070044; 20120080305; 20120128683; 20120150516;20120207362; 20120226185; 20120263393; 20120283502; 20120288143;20120302867; 20120316793; 20120321152; 20120321160; 20120323108;20130018596; 20130028496; 20130054214; 20130058548; 20130063434;20130064438; 20130066618; 20130085678; 20130102877; 20130102907;20130116540; 20130144192; 20130151163; 20130188830; 20130197401;20130211728; 20130226464; 20130231580; 20130237541; 20130243287;20130245422; 20130274586; 20130318546; 20140003696; 20140005518;20140018649; 20140029830; 20140058189; 20140063054; 20140063055;20140067740; 20140081115; 20140107935; 20140119621; 20140133720;20140133722; 20140148693; 20140155770; 20140163627; 20140171757;20140194726; 20140207432; 20140211593; 20140222113; 20140222406;20140226888; 20140236492; 20140243663; 20140247970; 20140249791;20140249792; 20140257073; 20140270438; 20140343397; 20140348412;20140350380; 20140355859; 20140371573; 20150010223; 20150012466;20150019241; 20150029087; 20150033245; 20150033258; 20150033259;20150033262; 20150033266; 20150073141; 20150073722; 20150080753;20150088015; 20150088478; 20150150530; 20150150753; 20150157266;20150161326; 20150161348; 20150174418; 20150196800; 20150199121;20150201849; 20150216762; 20150227793; 20150257700; 20150272448;20150287223; 20150294445; 20150297106; 20150306340; 20150317796;20150324545; 20150327813; 20150332015; 20150335303; 20150339459;20150343242; 20150363941; 20150379230; 20160004396; 20160004821;20160004957; 20160007945; 20160019693; 20160027178; 20160027342;20160035093; 20160038049; 20160038770; 20160048965; 20160067496;20160070436; 20160073991; 20160082319; 20160110517; 20160110866;20160110867; 20160113528; 20160113726; 20160117815; 20160117816;20160117819; 20160128661; 20160133015; 20160140313; 20160140707;20160151018; 20160155005; 20160166205; 20160180055; 20160203597;20160213947; 20160217586; 20160217595; 20160232667; 20160235324;20160239966; 20160239968; 20160246939; 20160263380; 20160284082;20160296287; 20160300352; 20160302720; 20160364859; 20160364860;20160364861; 20160366462; 20160367209; 20160371455; 20160374990;20170024886; 20170027539; 20170032524; 20170032527; 20170032544;20170039706; 20170053092; 20170061589; 20170076452; 20170085855;20170091418; 20170112577; 20170128032; 20170147578; 20170148213;20170168566; 20170178340; 20170193161; 20170198349; 20170202621;20170213339; 20170216595; 20170221206; and 20170231560.

fMRI

Functional magnetic resonance imaging or functional MRI (fMRI) is afunctional neuroimaging procedure using MRI technology that measuresbrain activity by detecting changes associated with blood flow(“Magnetic Resonance, a critical peer-reviewed introduction; functionalMRI”. European Magnetic Resonance Forum. Retrieved 17 Nov. 2014;Huettel, Song & McCarthy (2009)).

Yukiyasu Kamitani et al., Neuron (DOI: 10.1016j.neuron.2008.11.004) usedan image of brain activity taken in a functional MRI scanner to recreatea black-and-white image from scratch. See also ‘Mind-reading’ softwarecould record your dreams” By Celeste Biever. New Scientist, 12 Dec.2008.(www.newscientist.com/article/dn16267-mind-reading-software-could-record-your-dreams/)

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 6,622,036; 7,120,486;7,177,675; 7,209,788; 7,489,964; 7,697,979; 7,754,190; 7,856,264;7,873,411; 7,962,204; 8,060,181; 8,224,433; 8,315,962; 8,320,649;8,326,433; 8,356,004; 8,380,314; 8,386,312; 8,392,253; 8,532,756;8,562,951; 8,626,264; 8,632,750; 8,655,817; 8,679,009; 8,684,742;8,684,926; 8,698,639; 8,706,241; 8,725,669; 8,831,731; 8,849,632;8,855,773; 8,868,174; 8,915,871; 8,918,162; 8,939,903; 8,951,189;8,951,192; 9,026,217; 9,037,224; 9,042,201; 9,050,470; 9,072,905;9,084,896; 9,095,266; 9,101,276; 9,101,279; 9,135,221; 9,161,715;9,192,300; 9,230,065; 9,248,286; 9,248,288; 9,265,458; 9,265,974;9,292,471; 9,296,382; 9,302,110; 9,308,372; 9,345,412; 9,367,131;9,420,970; 9,440,646; 9,451,899; 9,454,646; 9,463,327; 9,468,541;9,474,481; 9,475,502; 9,489,854; 9,505,402; 9,538,948; 9,579,247;9,579,457; 9,615,746; 9,693,724; 9,693,734; 9,694,155; 9,713,433;9,713,444; 20030093129; 20030135128; 20040059241; 20050131311;20050240253; 20060015034; 20060074822; 20060129324; 20060161218;20060167564; 20060189899; 20060241718; 20070179534; 20070244387;20080009772; 20080091118; 20080125669; 20080228239; 20090006001;20090009284; 20090030930; 20090062676; 20090062679; 20090082829;20090132275; 20090137923; 20090157662; 20090164132; 20090209845;20090216091; 20090220429; 20090270754; 20090287271; 20090287272;20090287273; 20090287467; 20090290767; 20090292713; 20090297000;20090312808; 20090312817; 20090312998; 20090318773; 20090326604;20090327068; 20100036233; 20100049276; 20100076274; 20100094154;20100143256; 20100145215; 20100191124; 20100298735; 20110004412;20110028827; 20110034821; 20110092882; 20110106750; 20110119212;20110256520; 20110306845; 20110306846; 20110313268; 20110313487;20120035428; 20120035765; 20120052469; 20120060851; 20120083668;20120108909; 20120165696; 20120203725; 20120212353; 20120226185;20120253219; 20120265267; 20120271376; 20120296569; 20130031038;20130063550; 20130080127; 20130085678; 20130130799; 20130131755;20130158883; 20130185145; 20130218053; 20130226261; 20130226408;20130245886; 20130253363; 20130338803; 20140058528; 20140114889;20140135642; 20140142654; 20140154650; 20140163328; 20140163409;20140171757; 20140200414; 20140200432; 20140211593; 20140214335;20140243652; 20140276549; 20140279746; 20140309881; 20140315169;20140347265; 20140371984; 20150024356; 20150029087; 20150033245;20150033258; 20150033259; 20150033262; 20150033266; 20150038812;20150080753; 20150094962; 20150112899; 20150119658; 20150164431;20150174362; 20150174418; 20150196800; 20150227702; 20150248470;20150257700; 20150290453; 20150290454; 20150297893; 20150305685;20150324692; 20150327813; 20150339363; 20150343242; 20150351655;20150359431; 20150360039; 20150366482; 20160015307; 20160027342;20160031479; 20160038049; 20160048659; 20160051161; 20160051162;20160055304; 20160107653; 20160120437; 20160144175; 20160152233;20160158553; 20160206380; 20160213276; 20160262680; 20160263318;20160302711; 20160306942; 20160324457; 20160357256; 20160366462;20170027812; 20170031440; 20170032098; 20170042474; 20170043160;20170043167; 20170061034; 20170065349; 20170085547; 20170086727;20170087302; 20170091418; 20170113046; 20170188876; 20170196501;20170202476; 20170202518; and 20170206913.

Functional Near Infrared Spectroscopy (fNIRS)

fNIR is a non-invasive imaging method involving the quantification ofchromophore concentration resolved from the measurement of near infrared(NIR) light attenuation or temporal or phasic changes. NIR spectrumlight takes advantage of the optical window in which skin, tissue, andbone are mostly transparent to NIR light in the spectrum of 700-900 nm,while hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb) arestronger absorbers of light Differences in the absorption spectra ofdeoxy-Hb and oxy-Hb allow the measurement of relative changes inhemoglobin concentration through the use of light attenuation atmultiple wavelengths. Two or more wavelengths are selected, with onewavelength above and one below the isosbestic point of 810 nm at whichdeoxy-Hb and oxy-Hb have identical absorption coefficients. Using themodified Beer-Lambert law (mBLL), relative concentration can becalculated as a function of total photon path length. Typically, thelight emitter and detector are placed ipsilaterally on the subjectsskull so recorded measurements are due to back-scattered (reflected)light following elliptical pathways. The use of fNIR as a functionalimaging method relies on the principle of neuro-vascular coupling alsoknown as the hemodynamic response or blood-oxygen-level dependent (BOLD)response. This principle also forms the core of fMRI techniques. Throughneuro-vascular coupling, neuronal activity is linked to related changesin localized cerebral blood flow. fNIR and fMRI are sensitive to similarphysiologic changes and are often comparative methods. Studies relatingfMRI and fNIR show highly correlated results in cognitive tasks. fNIRhas several advantages in cost and portability over fMRI, but cannot beused to measure cortical activity more than 4 cm deep due to limitationsin light emitter power and has more limited spatial resolution. fNIRincludes the use of diffuse optical tomography (DOT/NIRDOT) forfunctional purposes. Multiplexing fNIRS channels can allow 2Dtopographic functional maps of brain activity (e.g. with Hitachi ETG4000or Artinis Oxymon) while using multiple emitter spacings may be used tobuild 3D tomographic maps.

Beste Yuksel and Robert Jacob, Brain Automated Chorales (BACh), ACM CHI2016, DOI: 10.1145/2858036.2858388, provides a system that helpsbeginners lean to play Bach chorales on piano by measuring how hardtheir brains are working. This is accomplished by estimating the brain'sworkload using functional Near-Infrared Spectroscopy (fNIRS), atechnique that measures oxygen levels in the brain—in this case in theprefrontal cortex. A brain that's working hard pulls in more oxygen.Sensors strapped to the player's forehead talk to a computer, whichdelivers the new music, one line at a time. See also “Mind-reading techhelps beginners quickly learn to play Bach.” By Anna Nowogrodzki, NewScientist, 9 Feb. 2016 available online atwww.newscientist.com/article/2076899-mind-reading-tech-helps-beginners-quickly-learn-to-play-bach/.

LORETA

Low-resolution brain electromagnetic tomography often referred as LORETAis a functional imaging technology usually using a linearly constrainedminimum variance vector beamformer in the time-frequency domain asdescribed in Gross et al., “Dynamic imaging of coherent sources:Studying neural interactions in the human brain,” PNAS 98, 694-699,2001. It allows to the image (mostly 3D) evoked and induced oscillatoryactivity in a variable time-frequency range, where time is takenrelative to a triggered event There are three categories of imagingrelated to the technique used for LORETA. See,wiki.besa.delindex.php?title=Source_Analysis_3D_1maging#Multiple_Source_Beamformer_0.28MSBF.29.The Multiple Source Beamformer (MSBF) is a tool for imaging brainactivity. It is applied in the time-frequency domain and based onsingle-trial data. Therefore, it can image not only evoked, but alsoinduced activity, which is not visible in time-domain averages of thedata. Dynamic Imaging of Coherent Sources (DICS) can find coherencebetween any two pairs of voxels in the brain or between an externalsource and brain voxels. DICS requires time-frequency-transformed dataand can find coherence for evoked and induced activity. The followingimaging methods provides an image of brain activity based on adistributed multiple source model: CLARA is an iterative application ofLORETA images, focusing the obtained 3D image in each iteration step.LAURA uses a spatial weighting function that has the form of a localautoregressive function. LORETA has the 3D Laplacian operatorimplemented as spatial weighting prior. sLORETA is an unweighted minimumnorm that is standardized by the resolution matrix. swLORETA isequivalent to sLORETA, except for an additional depth weighting. SSLOFOis an iterative application of standardized minimum norm images withconsecutive shrinkage of the source space. A User-defined volume imageallows experimenting with the different imaging techniques. It ispossible to specify user-defined parameters for the family ofdistributed source images to create a new imaging technique. If noindividual MRI is available, the minimum norm image is displayed on astandard brain surface and computed for standard source locations. Ifavailable, an individual brain surface is used to construct thedistributed source model and to image the brain activity. Unlikeclassical LORETA, cortical LORETA is not computed in a 3D volume, but onthe cortical surface. Unlike classical CLARA, cortical CLARA is notcomputed in a 3D volume, but on the cortical surface. The MultipleSource Probe Scan (MSPS) is a tool for the validation of a discretemultiple source model. The Source Sensitivity image displays thesensitivity of a selected source in the current discrete source modeland is, therefore, data independent.

See U.S. Patent Nos. and Pat. Appl. Nos.: U.S. Pat. Nos. 4,562,540;4,594,662; 5,650,726; 5,859,533; 6,026,173; 6,182,013; 6,294,917;6,332,087; 6,393,363; 6,534,986; 6,703,838; 6,791,331; 6,856,830;6,863,127; 7,030,617; 7,092,748; 7,119,553; 7,170,294; 7,239,731;7,276,916; 7,286,871; 7,295,019; 7,353,065; 7,363,164; 7,454,243;7,499,894; 7,648,498; 7,804,441; 7,809,434; 7,841,986; 7,852,087;7,937,222; 8,000,795; 8,046,076; 8,131,526; 8,174,430; 8,188,749;8,244,341; 8,263,574; 8,332,191; 8,346,365; 8,362,780; 8,456,166;8,538,700; 8,565,883; 8,593,154; 8,600,513; 8,706,205; 8,711,655;8,731,987; 8,756,017; 8,761,438; 8,812,237; 8,829,908; 8,958,882;9,008,970; 9,035,657; 9,069,097; 9,072,449; 9,091,785; 9,092,895;9,121,964; 9,133,709; 9,165,472; 9,179,854; 9,320,451; 9,367,738;9,414,749; 9,414,763; 9,414,764; 9,442,088; 9,468,541; 9,513,398;9,545,225; 9,557,439; 9,562,988; 9,568,635; 9,651,706; 9,675,254;9,675,255; 9,675,292; 9,713,433; 9,715,032; 20020000808; 20020017905;20030018277; 20030093004; 20040097802; 20040116798; 20040131998;20040140811; 20040145370; 20050156602; 20060058856; 20060069059;20060136135; 20060149160; 20060152227; 20060170424; 20060176062;20060184058; 20060206108; 20070060974; 20070159185; 20070191727;20080033513; 20080097235; 20080125830; 20080125831; 20080183072;20080242976; 20080255816; 20080281667; 20090039889; 20090054801;20090082688; 20090099783; 20090216146; 20090261832; 20090306534;20090312663; 20100010366; 20100030097; 20100042011; 20100056276;20100092934; 20100132448; 20100134113; 20100168053; 20100198519;20100231221; 20100238763; 20110004115; 20110050232; 20110160607;20110308789; 20120010493; 20120011927; 20120016430; 20120083690;20120130641; 20120150257; 20120162002; 20120215448; 20120245474;20120268272; 20120269385; 20120296569; 20130091941; 20130096408;20130141103; 20130231709; 20130289385; 20130303934; 20140015852;20140025133; 20140058528; 20140066739; 20140107519; 20140128763;20140155740; 20140161352; 20140163328; 20140163893; 20140228702;20140243714; 20140275944; 20140276012; 20140323899; 20150051663;20150112409; 20150119689; 20150137817; 20150145519; 20150157235;20150167459; 20150177413; 20150248615; 20150257648; 20150257649;20150301218; 20150342472; 20160002523; 20160038049; 20160040514;20160051161; 20160051162; 20160091448; 20160102500; 20160120436;20160136427; 20160187524; 20160213276; 20160220821; 20160223703;20160235983; 20160245952; 20160256109; 20160259085; 20160262623;20160298449; 20160334534; 20160345856; 20160356911; 20160367812;20170001016; 20170067323; 20170138132; and 20170151436.

Neurofeedback

Neurofeedback (NFB), also called neurotherapy or neurobiofeedback, is atype of biofeedback that uses real-time displays of brain activity-mostcommonly electroencephalography (EEG), to teach self-regulation of brainfunction. Typically, sensors are placed on the scalp to measureactivity, with measurements displayed using video displays or sound. Thefeedback may be in various other forms as well. Typically, the feedbackis sought to be presented through primary sensory inputs, but this isnot a limitation on the technique.

The applications of neurofeedback to enhance performance extend to thearts in fields such as music, dance, and acting. A study withconservatoire musicians found that alpha-theta training benefitted thethree music domains of musicality, communication, and technique.Historically, alpha-theta training, a form of neurofeedback, was createdto assist creativity by inducing hypnagogia, a “borderline waking stateassociated with creative insights”, through facilitation of neuralconnectivity. Alpha-theta training has also been shown to improve novicesinging in children. Alpha-theta neurofeedback, in conjunction withheart rate variability training, a form of biofeedback, has alsoproduced benefits in dance by enhancing performance in competitiveballroom dancing and increasing cognitive creativity in contemporarydancers. Additionally, neurofeedback has also been shown to instill asuperior flow state in actors, possibly due to greater immersion whileperforming.

Several studies of brain wave activity in experts while performing atask related to their respective area of expertise revealed certaincharacteristic telltale signs of so-called “flow” associated withtop-flight performance. Mihaly Csikszentmihalyi (University of Chicago)found that the most skilled chess players showed less EEG activity inthe prefrontal cortex, which is typically associated with highercognitive processes such as working memory and verbalization, during agame.

Chris Berka et al., Advanced Brain Monitoring, Carlsbad, Calif., TheInternational J. Sport and Society, vol 1, p 87, looked at thebrainwaves of Olympic archers and professional golfers. A few secondsbefore the archers fired off an arrow or the golfers hit the ball, theteam spotted a small increase in alpha band patterns. This maycorrespond to the contingent negative variation observed in evokedpotential studies, and the Bereitschaftspotential or BP (from German,“readiness potential”), also called the pre-motor potential or readinesspotential (RP), a measure of activity in the motor cortex andsupplementary motor area of the brain leading up to voluntary musclemovement Berka also trained novice marksmen using neurofeedback. Eachperson was hooked up to electrodes that tease out and display specificbrainwaves, along with a monitor that measured their heartbeat Bycontrolling their breathing and learning to deliberately manipulate thewaveforms on the screen in front of them, the novices managed to producethe alpha waves characteristic of the flow state. This, in turn, helpedthem improve their accuracy at hitting the targets.

Low Energy Neurofeedback System (LENS)

The LENS, or Low Energy Neurofeedback System, uses a very low powerelectromagnetic field, to carry feedback to the person receiving it. Thefeedback travels down the same wires carrying the brainwaves to theamplifier and computer. Although the feedback signal is weak, itproduces a measurable change in the brainwaves without conscious effortfrom the individual receiving the feedback. The system is softwarecontrolled, to receive input from EEG electrodes, to control thestimulation. Through the scalp. Neurofeedback uses a feedback frequencythat is different from, but correlates with, the dominant brainwavefrequency. When exposed to this feedback frequency, the EEG amplitudedistribution changes in power. Most of the time the brainwaves reduce inpower but at times they also increase in power. In either case theresult is a changed brainwave state, and much greater ability for thebrain to regulate itself.

Content-Based Brainwave Analysis

Memories are not unique. Janice Chen, Nature Neuroscience, DOI:10.1038/nn.4450, showed that when people describe the episode fromSherlock Holmes drama, their brain activity patterns were almost exactlythe same as each others, for each scene. Moreover, there's also evidencethat, when a person tells someone else about it they implant that sameactivity into their brain as well. Moreover, research in which peoplewho have not seen a movie listen to someone else's description of it,Chen et al. have found that the listeners brain activity looks much likethat of the person who has seen it See also “Our brains record andremember things in exactly the same way” by Andy Coghlan, New Scientist,Dec. 5, 2016(www.newscientist.com/article/2115093-our-brains-record-and-remember-things-in-exactly-the-same-way/)

Brian Pasley, Frontiers in Neuroengineering, doi.orglwhb, developed atechnique for reading thoughts. The team hypothesized that hearingspeech and thinking to oneself might spark some of the same neuralsignatures in the brain. They supposed that an algorithm trained toidentify speech heard out loud might also be able to identify words thatare thought In the experiment, the decoder trained on speech was able toreconstruct which words several of the volunteers were thinking, usingneural activity alone. See also “Hearing our inner voice” by HelenThomson. New Scientist, Oct. 29, 2014(www.newscientist.com/article/mg22429934-000-brain-decoder-can-eavesdrop-on-your-inner-voice/)

Jack Gallant et al. were able to detect which of a set of images someonewas looking at from a brain scan, using software that compared thesubjects brain activity while looking at an image with that capturedwhile they were looking at “training” photographs. The program thenpicked the most likely match from a set of previously unseen pictures.

Ann Graybiel and Mark Howe used electrodes to analyze brainwaves in theventromedial striatum of rats while they were taught to navigate a maze.As rats were learning the task, their brain activity showed bursts offast gamma waves. Once the rats mastered the task, their brainwavesslowed to almost a quarter of their initial frequency, becoming betawaves. Graybiel's team posited that this transition reflects whenlearning becomes a habit.

Bernard Balleine, Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.1113158108. See also “Habits form when brainwaves slowdown” by Wendy Zukerman. New Scientist, Sep. 26, 2011(www.newscientist.com/article/dn20964-habits-form-when-brainwaves-slow-down/)posits that the slower brainwaves may be the brain weeding out excessactivity to refine behavior. He suggests it might be possible to boostthe rate at which they learn a skill by enhancing such beta-waveactivity.

U.S. Pat. No. 9,763,592 provides a system for instructing a userbehavior change comprising: collecting and analyzing bioelectricalsignal datasets; and providing a behavior change suggestion based uponthe analysis. A stimulus may be provided to prompt an action by theuser, which may be visual, auditory, or haptic. See also U.S. Pat. No.9,622,660, 20170041699; 20130317384; 20130317382; 20130314243;20070173733; and 20070066914.

The chess game is a good example of a cognitive task which needs a lotof training and experience. A number of EEG studies have been done onchess players. Pawel Stepien, Wlodzimierz Klonowski and Nikolay Suvorov,Nonlinear analysis of EEG in chess players, EPJ Nonlinear BiomedicalPhysics 20153:1, showed better applicability of Higuchi FractalDimension method for analysis of EEG signals related to chess tasks thanthat of Sliding Window Empirical Mode Decomposition. The paper showsthat the EEG signal during the game is more complex, non-linear, andnon-stationary even when there are no significant differences betweenthe game and relaxed state in the contribution of different EEG bands tototal power of the signal. There is the need of gathering more data frommore chess experts and of comparing them with data from novice chessplayers. See also Junior, L. R. S., Cesar, F. H. G., Rocha, F. T., andThomaz, C. E. EEG and Eye Movement Maps of Chess Players. Proceedings ofthe Sixth International Conference on Pattern Recognition Applicationsand Methods. (ICPRAM 2017) pp. 343-441.(fei.edu.br/˜cet/icpram17_LaercioJunior.pdf).

Estimating EEG-based functional connectivity provides a useful tool forstudying the relationship between brain activity and emotional states.See You-Yun Lee, Shulan Hsieh. Classifying Different Emotional States byMeans of EEG-Based Functional Connectivity Patterns. Apr. 17, 2014,(doi.org/10.1371journal.pone.0095415), which aimed to classify differentemotional states by means of EEG-based functional connectivity patterns,and showed that the EEG-based functional connectivity change wassignificantly different among emotional states. Furthermore, theconnectivity pattern was detected by pattern classification analysisusing Quadratic Discriminant Analysis. The results indicated that theclassification rate was better than chance. Estimating EEG-basedfunctional connectivity provides a useful tool for studying therelationship between brain activity and emotional states.

Neuromodulation/Neuroenhancement

Neuromodulation is the alteration of nerve activity through targeteddelivery of a stimulus, such as electrical stimulation or chemicalagents, to specific neurological sites in the body. It is carried out tonormalize- or modulate-nervous tissue function. Neuromodulation is anevolving therapy that can involve a range of electromagnetic stimulisuch as a magnetic field (TMS, rTMS), an electric current (TES, e.g.,tDCS, HD-tDCS, tACS, osc-tDCS, electrosleep), or a drug instilleddirectly in the subdural space (intrathecal drug delivery). Emergingapplications involve targeted introduction of genes or gene regulatorsand light (optogenetics). The most clinical experience has been withelectrical stimulation. Neuromodulation, whether electrical or magnetic,employs the body's natural biological response by stimulating nerve cellactivity that can influence populations of nerves by releasingtransmitters, such as dopamine, or other chemical messengers such as thepeptide Substance P, that can modulate the excitability and firingpatterns of neural circuits. There may also be more directelectrophysiological effects on neural membranes. According to someapplications, the end effect is a “normalization” of a neural networkfunction from its perturbed state. Presumed mechanisms of action forneurostimulation include depolarizing blockade, stochastic normalizationof neural firing, axonal blockade, reduction of neural firing keratosis,and suppression of neural network oscillations. Although the exactmechanisms of neurostimulation are not entirely clear, the empiricaleffectiveness has led to considerable application clinically.

Neuroenhancement refers to the targeted enhancement and extension ofcognitive, affective, and motor abilities based on an understanding oftheir underying neurobiology in healthy persons who do not have anymental illness. As such, it can be thought of as an umbrella term thatencompasses pharmacological and non-pharmacological methods of improvingcognitive, affective, and motor functionality. Critically, for any agentto qualify as a neuroenhancer, it must reliably engender substantialcognitive, affective, or motor benefits beyond normal functioning inhealthy individuals (or in select groups of individuals havingpathology), while causing few side effects: at most at the level ofcommonly used comparable legal substances or activities, such ascaffeine, alcohol, and sleep-deprivation. Pharmacologicalneuroenhancement agents include the well-validated nootropics, such asracetam, vinpocetine, and phosphatidylserine, as well as other drugsused for treating patients suffering from neurological disorders.Non-pharmacological measures include non-invasive brain stimulation,which has been employed to improve various cognitive and affectivefunctions, and brain-machine interfaces, which hold much potential toextend the repertoire of motor and cognitive actions available tohumans.

Brain Stimulation

The entrainment hypothesis, suggests the possibility of inducing aparticular oscillation frequency in the brain using an externaloscillatory force (e.g., rTMS, tDCS, tACS, binaural beats, isochronictones, light stimulation). The physiological basis of oscillatorycortical activity lies in the timing of the interacting neurons; whengroups of neurons synchronize their firing activities, brain rhythmsemerge, network oscillations are generated, and the basis forinteractions between brain areas may develop. Synchronization ofspatially separated lobes of the brain may also play a role. Because ofthe variety of experimental protocols for brain stimulation, limits ondescriptions of the actual protocols employed, and limited controls,consistency of reported studies is lacking, and extrapolability islimited. Thus, while there is some consensus in various aspects of theeffects of extra cranial brain stimulation, the results achieved have adegree of uncertainty dependent on details of implementation. On theother hand, within a specific experimental protocol, it is possible toobtain statistically significant and repeatable results. This impliesthat feedback control might be effective to control implementation ofthe stimulation for a given purpose; however, prior studies that employfeedback control are lacking.

Changes in the neuronal threshold result from changes in membranepermeability (Liebetanz et al., 2002), which influence the response ofthe task-related network. The same mechanism of action may beresponsible for both TES methods and TMS, i.e., the induction of noisein the system. However, the neural activity induced by TES will behighly influenced by the state of the system because it is aneuromodulatory method (Paulus, 2011), and its effect will depend on theactivity of the stimulated area. Therefore, the final result will dependstrongly on the task characteristics, the system state and the way inwhich TES will interact with such a state.

In TMS, the magnetic pulse causes a rapid increase in current flow,which can in some cases cause and above-threshold depolarization of cellmembranes affected by the current, triggering an action potential, andleading to the transsynaptic depolarization or hyperpolarization ofconnected cortical neurons, depending on their natural response to thefiring of the stimulated neuron(s). Therefore, TMS activates a neuralpopulation that, depending on several factors, can be congruent(facilitate) or incongruent (inhibit) with task execution. TES induces apolarization of cortical neurons at a subthreshold level that is tooweak to evoke an action potential. However, by inducing a polarity shiftin the intrinsic neuronal excitability, TES can alter the spontaneousfiring rate of neurons and modulate the response to afferent signals. Inthis sense, TES-induced effects are even more bound to the state of thestimulated area that is determined by the conditions. In short, NIBSleads to a stimulation-induced modulation of the state that can besubstantially defined as noise induction. Induced noise will not be justrandom activity, but will depend on the interaction of many parameters,from the characteristics of the stimulation to the state.

The noise induced by NIBS will be influenced by the state of the neuralpopulation of the stimulated area. Although the types and number ofneurons “triggered” by NIBS are theoretically random, the induced changein neuronal activity is likely to be correlated with ongoing activity,yet even if we are referring to a non-deterministic process, the noiseintroduced will not be a totally random element Because it will bepartially determined by the experimental variables, the level of noisethat will be introduced by the stimulation and by the context can beestimated, as well as the interaction between the two levels of noise(stimulation and context). Although, HD-tDCS made a significantly morefocused spatial application of TES possible, generally, knowntranscranial stimulation does not permit stimulation with a focused andhighly targeted signal to a clearly defined area of the brain toestablish a unique brain-behavior relationship; therefore, the knownintroduced stimulus activity in the brain stimulation is ‘noise.’

Cosmetic neuroscience has emerged as a new field of research. RoyHamilton, Samuel Messing, and Anjan Chatterjee, “Rethinking the thinkingcap—Ethics of neural enhancement using noninvasive brain stimulation.”Neurology, Jan. 11, 2011, vol. 76 no. 2 187-193.(www.neurology.org/content/76/2/187.) discuss the use noninvasive brainstimulation techniques such as transcranial magnetic stimulation andtranscranial direct current stimulation to enhance neurologic function:cognitive skills, mood, and social cognition.

Electrical brain stimulation (EBS), also known as, focal brainstimulation (FBS), is a form of clinical neurobiology electrotherapyused to stimulate a neuron or neural network in the brain through thedirect or indirect excitation of cell membranes using an electriccurrent See: en.wikipedia.org/wiki/Electrical_brain_stimulation; U.S.Patent Nos. and Pub. App. Nos. U.S. Pat. Nos. 7,753,836; 7,94673;8,545,378; 9,345,901; 9,610,456; 9,694,178; 20140330337; 20150112403;and 20150119689.

Motor skills can be affected by CNS stimulation.

See, U.S. Pub. App. Nos. U.S. Pat. Nos. 5,343,871; 5,742,748; 6,057,846;6,390,979; 6,644,976; 6,656,137; 7,063,535; 7,558,622; 7,618,381;7,733,224; 7,829,562; 7,863,272; 8,016,597; 8,065,240; 8,069,125;8,108,036; 8,126,542; 8,150,796; 8,195,593; 8,356,004; 8,449,471;8,461,988; 8,525,673; 8,525,687; 8,531,291; 8,591,419; 8,606,592;8,615,479; 8,680,991; 8,682,449; 8,706,518; 8,747,336; 8,750,971;8,764,651; 8,784,109; 8,858,440; 8,862,236; 8,938,289; 8,962,042;9,005,649; 9,064,036; 9,107,586; 9,125,788; 9,138,579; 9,149,599;9,173,582; 9,204,796; 9,211,077; 9,265,458; 9,351,640; 9,358,361;9,380,976; 9,403,038; 9,418,368; 9,468,541; 9,495,684; 9,545,515;9,549,691; 9,560,967; 9,577,992; 9,590,986; 20030068605; 20040072133;20050020483; 20050032827; 20050059689; 20050153268; 20060014753;20060052386; 20060106326; 20060191543; 20060229164; 20070031798;20070138886; 20070276270; 20080001735; 20080004904; 20080243005;20080287821; 20080294019; 20090005654; 20090018407; 20090024050;20090118593; 20090119154; 20090132275; 20090156907; 20090156955;20090157323; 20090157481; 20090157482; 20090157625; 20090157660;20090157751; 20090157813; 20090163777; 20090164131; 20090164132;20090164302; 20090164401; 20090164403; 20090164458; 20090164503;20090164549; 20090171164; 20090172540; 20090221928; 20090267758;20090271011; 20090271120; 20090271347; 20090312595; 20090312668;20090318773; 20090318779; 20090319002; 20100004762; 20100015583;20100017001; 20100022820; 20100041958; 20100042578; 20100063368;20100069724; 20100076249; 20100081860; 20100081861; 20100100036;20100125561; 20100130811; 20100145219; 20100163027; 20100163035;20100168525; 20100168602; 20100280332; 20110015209; 20110015469;20110082154; 20110105859; 20110115624; 20110152284; 20110178441;20110181422; 20110288119; 20120092156; 20120092157; 20120130300;20120143104; 20120164613; 20120177716; 20120316793; 20120330109;20130009783; 20130018592; 20130034837; 20130053656; 20130054215;20130085678; 20130121984; 20130132029; 20130137717; 20130144537;20130184728; 20130184997; 20130211291; 20130231574; 20130281890;20130289385; 20130330428; 20140039571; 20140058528; 20140077946;20140094720; 20140104059; 20140148479; 20140155430; 20140163425;20140207224; 20140235965; 20140249429; 20150025410; 20150025422;20150068069; 20150071907; 20150141773; 20150208982; 20150265583;20150290419; 20150294067; 20150294085; 20150294086; 20150359467;20150379878; 20160001096; 20160007904; 20160007915; 20160030749;20160030750; 20160067492; 20160074657; 20160120437; 20160140834;20160198968; 20160206671; 20160220821; 20160303402; 20160351069;20160360965; 20170046971; 20170065638; 20170080320; 20170084187;20170086672; 20170112947; 20170127727; 20170131293; 20170143966;20170151436; 20170157343; and 20170193831.

-   Abraham, W. C., 2008. Metaplasticity: tuning synapses and networks    for plasticity. Nature Reviews Neuroscience 9, 387.-   Abrahamyan, A., Clifford, C. W., Arabzadeh, E., Harris, J. A., 2011.    Improving visual sensitivity with subthreshold transcranial magnetic    stimulation. J. Neuroscience 31, 3290-3294.-   Adrian, E. D., 1928. The Basis of Sensation. W. W. Norton, New York.-   Amassian, V. E., Cracco, R. Q., Maccabee, P. J., Cracco, J. B.,    Rudell, A, Eberle, L., 1989. Suppression of visual perception by    magnetic coil stimulation of human occipital cortex.    Electroencephalography and Clin. Neurophysiology 74, 458-462.-   Amassian, V. E., Eberle, L., Maccabee, P. J., Cracco, R. Q., 1992.    Modelling magnetic coil excitation of human cerebral cortex with a    peripheral nerve immersed in a brain-shaped volume conductor the    significance of fiber bending in excitation. Electroencephalography    and Clin. Neurophysiology 85, 291-301.-   Antal, A., Boros, K., Poreisz, C., Chaieb, L., Temey, D., Paulus,    W., 2008. Comparatively weak after-effects of transcranial    alternating current stimulation (tACS) on cortical excitability in    humans. Brain Stimulation 1, 97-105.-   Antal, A., Nitsche, M. A., Kruse, W., Kincses, T. Z., Hoffmann, K.    P., Paulus, W., 2004. Direct current stimulation over V5 enhances    visuomotor coordination by improving motion perception in humans. J.    Cognitive Neuroscience 16, 521-527.-   Ashbridge, E., Walsh, V., Cowey, A., 1997. Temporal aspects of    visual search studied by transcranial magnetic stimulation.    Neuropsychologia 35, 1121-1131.-   Barker, A. T., Freeston, I. L., Jalinous, R., Jarratt, J. A., 1987.    Magnetic stimulation of the human brain and peripheral nervous    system: an inroduction and the results of an initial clinical    evaluation. Neurosurgery 20, 100-109.-   Barker, A. T., Jalinous, R., Freeston, I. L., 1985. Non-invasive    magnetic stimulation of human motor cortex. Lancet 1, 1106-1107.-   Bi, G., Poo, M., 2001. Synaptic modification by correlated activity:    Hebb's postulate revisited. Annual Review of Neuroscience 24,    139-166.-   Bialek, W., Rieke, F., 1992. Reliability and information    transmission in spiking neurons. Trends in Neurosciences 15,    428-434.-   Bienenstock, E. L., Cooper, L. N., Munro, P. W., 1982. Theory for    the development of neuron selectivity: orientation specificity and    binocular interaction in visual cortex. J. Neuroscience 2, 32-48.-   Bindman, L. J., Lippold, O. C., Milne, A. R., 1979. Prolonged    changes in excitability of pyramidal tract neurones in the cat a    post-synaptic mechanism. J. Physiology 286, 457-477.-   Bindman, L. J., Lippold, O. C., Redfeam, J. W., 1962. Long-lasting    changes in the level of the electrical activity of the cerebral    cortex produced by polarizing currents. Nature 196, 584-585.-   Bindman, L. J., Lippold, O. C., Redfeam, J. W., 1964. The action of    brief polarizing currents on the cerebral cortex of the rat (1)    during current flow and (2) in the production of long-lasting    after-effects. J. Physiology 172, 369-382.-   Brignani, D., Ruzzoli, M., Mauri, P., Miniussi, C., 2013. Is    transcranial alternating current stimulation effective in modulating    brain oscillations? PLoS ONE 8, e56589. Buzsaki, G., 2006. Rhythms    of the Brain. Oxford University Press, Oxford.-   Canolty, R. T., Knight, R. T., 2010. The functional role of    cross-frequency coupling. Trends in Cognitive Sciences 14, 506-515.-   Carandini, M., Ferster, D., 1997. A tonic hyperpolarization    underlying contrast adaptation in cat visual cortex Science 276,    949-952.-   Cattaneo, L., Sandrini, M., Schwarzbach, J., 2010. State-dependent    TMS reveals a hierarchical representation of observed acts in the    temporal, parietal, and premotor cortices. Cerebral Cortex 20,    2252-2258.-   Cattaneo, Z., Rota, F., Vecchi, T., Silvanto, J., 2008. Using    state-dependency of trans-cranial magnetic stimulation (TMS) to    investigate letter selectivity in the left posterior parietal    cortex: a comparison of TMS-priming and TMS-adaptation paradigms.    Eur. J. Neuroscience 28, 1924-1929.-   Chambers, C. D., Payne, J. M., Stokes, M. G., Mattingley, J.    B., 2004. Fast and slow parietal pathways mediate spatial attention.    Nature Neuroscience 7, 217-218.-   Corthout, E., Utti, B., Walsh, V., Hallett, M., Cowey, A., 1999.    Timing of activity in early visual cortex as revealed by    transcranial magnetic stimulation. Neuroreport 10, 2631-2634.-   Creutzfeldt, O. D., Fromm, G. H., Kapp, H., 1962. Influence of    transcortical d-c currents on cortical neuronal activity.    Experimental Neurology 5, 436-452.-   Deans, J. K., Powell, A. D., Jefferys, J. G., 2007. Sensitivity of    coherent oscillations in rat hippocampus to AC electic fields. J.    Physiology 583, 555-565.-   Dockery, C. A., Hueckel-Weng, R., Birbaumer, N., Plewnia, C., 2009.    Enhancement of planning ability by transcranial direct current    stimulation. J. Neuroscience 29, 7271-7277.-   Ermentrout, G. B., Galan, R. F., Urban, N. N., 2008. Reliability,    synchrony and noise. Trends in Neurosciences 31, 428-434.-   Epstein, C. M., Rothwell, J. C., 2003. Therapeutic uses of rTMS.    Cambridge University Press, Cambridge, pp. 246-263.-   Faisal, A. A., Selen, L. P., Wolpert, D. M., 2008. Noise in the    nervous system. Nature Reviews Neuroscience 9, 292-303.-   Ferbert, A, Caramia, D., Priori, A., Bertolasi, L., Rothwell, J.    C., 1992. Cortical projection to erector spinae muscles in man as    assessed by focal transcranial magnetic stimulation.    Electroencephalography and Clin. Neurophysiology 85, 382-387.-   Fertonani, A., Pirulli, C., Miniussi, C., 2011. Random noise    stimulation improves neuroplasticity in perceptual learning. J.    Neuroscience 31, 15416-15423. Feurra, M., Galli, G., Rossi,    S., 2012. Transcranial alternating current stimulation affects    decision making. Frontiers in Systems Neuroscience 6, 39.-   Guyonneau, R., Vanrullen, R., Thorpe, S. J., 2004. Temporal codes    and sparse representations: a key to understanding rapid processing    in the visual system. J. Physiology, Paris 98, 487-497.-   Hallett, M., 2000. Transcranial magnetic stimulation and the human    brain. Nature 406, 147-150.-   Harris, I. M., Miniussi, C., 2003. Parietal lobe contribution to    mental rotation demonstrated with rTMS. J. Cognitive Neuroscience    15, 315-323.-   Harris, J. A., Clifford, C. W., Miniussi, C., 2008. The functional    effect of transcranial magnetic stimulation: signal suppression or    neural noise generation. J. Cognitive Neuroscience 20, 734-740.-   Hebb, D. O., 1949. The Organization of Behavior A Neuropsychological    Theory. Wiley, New York.-   Hutcheon, B., Yarom, Y., 2000. Resonance, oscillation and the    intrinsic frequency preferences of neurons. Trends in Neurosciences    23, 216-222.-   Jacobson, L., Koslowsky, M., Lavidor, M., 2011. tDCS polarity    effects in motor and cognitive domains: a meta-analytical review.    Experimental Brain Research 216, 1-10.-   Joundi, R. A., Jenkinson, N., Brittain, J. S., Aziz, T. Z., Brown,    P., 2012. Driving oscillatory activity in the human cortex enhances    motor performance. Current Biology 22, 403-407.-   Kahn, I., Pascual-Leone, A., Theoret, H., Fregni, F., Clark, D.,    Wagner, A. D., 2005. Transient disruption of ventrolateral    prefrontal cortex during verbal encoding affects subsequent memory    performance. J. Neurophysiology 94, 688-698.-   Kanai, R., Chaieb, L., Antal, A, Walsh, V., Paulus, W., 2008.    Frequency-dependent electiical stimulation of the visual cortex.    Current Biology 18, 1839-1843.-   Kitajo, K., Doesburg, S. M., Yamanaka, K., Nozaki, D., Ward, L. M.,    Yamamoto, Y., 2007. Noise-induced large-scale phase synchronization    of human-brain activity associated with behavioral stochastic    resonance. EPL—Europhysics Letters, 80.-   Kitajo, K., Nozaki, D., Ward, L. M., Yamamoto, Y., 2003. Behavioral    stochastic resonance within the human brain. Physical Review Letters    90, 218103.-   Landi, D., Rossini, P. M., 2010. Cerebral restorative plasticity    from normal aging to brain diseases: a never-ending story.    Restorative Neurology and Neuroscience 28, 349-366.-   Lang, N., Rothkegel, H., Reiber, H., Hasan, A., Sueske, E., Tergau,    F., Ehrenreich, H., Wuttke, W., Paulus, W., 2011. Circadian    modulation of G ABA-mediated cortical inhibition. Cerebral Cortex    21, 2299-2306.-   Laycock, R., Crewther, D. P., Fitzgerald, P. B., Crewther, S.    G., 2007. Evidence for fast signals and later processing in human    V1N2 and V5/MT+. A TMS study of motion perception. J.    Neurophysiology 98, 1253-1262.-   Liebetanz, D., Nitsche, M. A., Tergau, F., Paulus, W., 2002.    Pharmacological approach to the mechanisms of transcranial    DC-stimulation-induced after-effects of human motor cortex    excitability. Brain 125, 2238-2247.-   Longtin, A., 1997. Autonomous stochastic resonance in bursting    neurons. Physical Review E 55, 868-876.-   Manenti, R., Cappa, S. F., Rossini, P. M., Miniussi, C., 2008. The    role of the prefrontal cortex in sentence comprehension: an rTMS    study. Cortex 44, 337-344.-   Marzi, C. A., Miniussi, C., Maravita, A., Bertolasi, L., Zanette,    G., Rothwell, J. C., Sanes, J. N., 1998. Transcranial magnetic    stimulation selectively impairs interhemispheric transfer of    visuo-motor information in humans. Experimental Brain Research 118,    435438.-   Masquelier, T., Thorpe, S. J., 2007. Unsupervised learning of visual    features through spike timing dependent plasticity. PLOS    Computational Biology 3, e31.-   Miniussi, C., Brignani, D., Pellicciari, M. C., 2012a. Combining    transcranial electrical stimulation with electroencephalography: a    multimodal approach. Clin. EEG and Neuroscience 43, 184-191.-   Miniussi, C., Paulus, W., Rossini, P. M., 2012b. Transcranial Brain    Stimulation. CRC Press, Boca Raton, Fla.-   Miniussi, C., Ruzzoli, M., Walsh, V., 2010. The mechanism of    transcranial magnetic stimulation in cognition. Cortex 46, 128-130.-   Moliadze, V., Zhao, Y., Eysel, U., Funke, K., 2003. Effect of    transcranial magnetic stimulation on single-unit activity in the cat    primary visual cortex. J. Physiology 553, 665-679.-   Moss, F., Ward, L. M., Sannita, W. G., 2004. Stochastic resonance    and sensory information processing: a tutorial and review of    application. Clin. Neurophysiology 115, 267-281.-   Mottaghy, F. M., Gangitano, M., Krause, B. J., Pascual-Leone,    A., 2003. Chronometry of parietal and prefrontal activations in    verbal working memory revealed by transcranial magnetic stimulation.    Neuroimage 18, 565-575.-   Nachmias, J., Sansbury, R. V., 1974. Grating contrast discrimination    may be better than detection. Vision Research 14, 1039-1042.-   Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A, Lang,    N., Antal, A, Paulus, W., Hummel, F., Boggio, P. S., Fregni, F.,    Pascual-Leone, A., 2008. Transcranial direct current stimulation:    state of the art 2008. Brain Stimulation 1, 206-223.-   Nitsche, M. A., Liebetanz, D., Lang, N., Antal, A, Tergau, F.,    Paulus, W., 2003a. Safety criteria for transcranial direct current    stimulation (tDCS) in humans. Clin. Neurophysiology 114, 2220-2222,    author reply 2222-2223.-   Nitsche, M. A., Niehaus, L., Hoffmann, K. T., Hengst, S., Liebetanz,    D., Paulus, W., Meyer, B. U., 2004. MRI study of human brain exposed    to weak direct current stimulation of the frontal cortex. Clin.    Neurophysiology 115, 2419-2423.-   Nitsche, M. A., Nitsche, M. S., Klein, C. C., Tergau, F.,    Rothwell, J. C., Paulus, W., 2003b. Level of action of cathodal DC    polarisation induced inhibition of the human motor cortex. Clin.    Neurophysiology 114, 600-604.-   Nitsche, M. A., Paulus, W., 2000. Excitability changes induced in    the human motor cortex by weak transcranial direct current    stimulation. J. Physiology 527 (Pt 3), 633-639.-   Nitsche, M. A., Paulus, W., 2011. Transcranial direct current    stimulation—update 2011. Restorative Neurology and Neuroscience 29,    463-492.-   Nitsche, M. A., Seeber, A., Frommann, K., Klein, C. C., Rochford,    C., Nitsche, M. S., Fricke, K., Liebetanz, D., Lang, N., Antal, A.,    Paulus, W., Tergau, F., 2005. Modulating parameters of excitability    during and aftertranscranial direct current stimulation of the human    motor cortex. J. Physiology 568, 291-303.-   Pascual-Leone, A., Walsh, V., Rothwell, J., 2000. Transcranial    magnetic stimulation in cognitive neuroscience-virtual lesion,    chronometry, and functional connectivity. Current Opinion in    Neurobiology 10, 232-237.-   Pasley, B. N., Allen, E. A., Freeman, R. D., 2009. State-dependent    variability of neuronal responses to transcranial magnetic    stimulation of the visual cortex. Neuron 62, 291-303.-   Paulus, W., 2011. Transcranial electrical stimulation (tES-tDCS;    tRNS, tACS) methods. Neuropsychological Rehabilitation 21, 602-617.-   Plewnia, C., Rilk, A. J., Soekadar, S. R., Arfeller, C., Huber, H.    S., Sauseng, P., Hummel, F., Gerloff, C., 2008. Enhancement of    long-range EEG coherence by synchronous bifocal transcranial    magnetic stimulation. European J. Neuroscience 27, 1577-1583.-   Pogosyan, A., Gaynor, L. D., Eusebio, A., Brown, P., 2009. Boosting    cortical activity at Beta-band frequencies slows movement in humans.    Current Biology 19, 1637-1641.-   Priori, A., Berardelli, A., Rona, S., Accomero, N., Manfredi,    M., 1998. Polarization of the human motor cortex through the scalp.    Neuroreport 9, 2257-2260.-   Radman, T., Datta, A., Peterchev, A. V., 2007. In vitro modulation    of endogenous rhythms by AC electric fields: syncing with clinical    brain stimulation. J. Physiology 584, 369-370.-   Rahnev, D. A., Maniscalco, B., Luber, B., Lau, H., Lisanby, S.    H., 2012. Direct injection of noise to the visual cortex decreases    accuracy but increases decision confidence. J. Neurophysiology 107,    1556-1563.-   Reato, D., Rahman, A., Bikson, M., Parra, L. C., 2010. Low-intensity    electrical stimulation affects network dynamics by modulating    population rate and spike timing. J. Neuroscience 30, 15067-15079.-   Ridding, M. C., Ziemann, U., 2010. Determinants of the induction of    cortical plasticity by non-invasive brain stimulation in healthy    subjects. J. Physiology 588, 2291-2304.-   Rosanova, M., Casali, A., Bellina, V., Resta, F., Mariotti, M.,    Massimini, M., 2009. Natural frequencies of human corticothalamic    circuits. J. Neuroscience 29, 7679-7685.-   Rossi, S., Hallett, M., Rossini, P. M., Pascual-Leone, A., Safety of    TMS Consensus Group, 2009. Safety, ethical considerations, and    application guidelines forthe use of transcranial magnetic    stimulation in clinical practice and research. Clin. Neurophysiology    120, 2008-2039.-   Roth, B. J., 1994. Mechanisms for electrical stimulation of    excitable tissue. Critical Reviews in Biomedical Engineering 22,    253-305.-   Rothwell, J. C., Day, B. L., Thompson, P. D., Dick, J. P.,    Marsden, C. D., 1987. Some experiences of techniques for stimulation    of the human cerebral motor cortex through the scalp. Neurosurgery    20, 156-163.-   Ruohonen, J., 2003. Background physics for magnetic stimulation.    Supplements to Clin. Neurophysiology 56, 3-12.-   Ruzzoli, M., Abrahamyan, A., Clifford, C. W., Marzi, C. A.,    Miniussi, C., Harris, J. A., 2011. The effect of TMS on visual    motion sensitivity: an increase in neural noise or a decrease in    signal strength. J. Neurophysiology 106, 138-143.-   Ruzzoli, M., Marzi, C. A., Miniussi, C., 2010. The neural mechanisms    of the effects of transcranial magnetic stimulation on    perception. J. Neurophysiology 103, 2982-2989.-   Sack, A. T., Linden, D. E., 2003. Combining transcranial magnetic    stimulation and functional imaging in cognitive brain research:    possibilities and limitations. Brain Research: Brain Research    Reviews 43, 41-56.-   Sandrini, M., Umilta, C., Rusconi, E., 2011. The use of transcranial    magnetic stimulation in cognitive neuroscience: a new synthesis of    methodological issues. Neuroscience and Biobehavioral Reviews 35,    516-536.-   Schutter, D. J., Hortensius, R., 2010. Retinal origin of phosphenes    to transcranial alternating current stimulation. Clin.    Neurophysiology 121, 1080-1084.-   Schwarzkopf, D. S., Silvanto, J., Rees, G., 2011. Stochastic    resonance effects reveal the neural mechanisms of transcranial    magnetic stimulation. J. Neuro-science 31, 3143-3147.-   Schwiedrzik, C. M., 2009. Retina or visual cortex? The site of    phosphene induction by transcranial alternating current stimulation.    Frontiers in Integrative Neuro-science 3, 6.-   Sclar, G., Lennie, P., DePriest, D. D., 1989. Contrast adaptation in    stiate cortex of macaque. Vision Research 29, 747-755.-   Seyal, M., Masuoka, L. K., Browne, J. K., 1992. Suppression of    cutaneous perception by magnetic pulse stimulation of the human    brain. Electroencephalography and Clin. Neurophysiology 85, 397-401.-   Siebner, H. R., Lang, N., Rizzo, V., Nitsche, M. A., Paulus, W.,    Lemon, R. N., Rothwell, J. C., 2004. Preconditioning of    low-frequency repetitive transcranial magnetic stimulation with    transcranial direct current stimulation: evidence for homeostatic    plasticity in the human motor cortex. The J. Neuroscience 24,    3379-3385.-   Silvanto, J., Muggleton, N., Walsh, V., 2008. State-dependency in    brain stimulation studies of perception and cognition. Trends in    Cognitive Sciences 12, 447-454.-   Silvanto, J., Muggleton, N. G., Cowey, A., Walsh, V., 2007. Neural    adaptation reveals state-dependent effects of transcranial magnetic    stimulation. Eur. J. Neuroscience 25, 1874-1881.-   Solomon, J. A., 2009. The history of dipper functions. Attention,    Perception, and Psychophysics 71, 435-443.-   Stein, R. B., Gossen, E. R., Jones, K. E., 2005. Neuronal    variability: noise or part of the signal? Nature Reviews    Neuroscience 6, 389-397.-   Terney, D., Chaieb, L., Moliadze, V., Antal, A., Paulus, W., 2008.    Increasing human brain excitability by transcranial high-frequency    random noise stimulation. J. Neuroscience 28, 14147-14155.-   Thut, G., Miniussi, C., 2009. New insights into rhythmic brain    activity from TMS-EEG studies. Trends in Cognitive Sciences 13,    182-189.-   Thut, G., Miniussi, C., Gross, J., 2012. The functional importance    of rhythmic activity in the brain. Current Biology 22, R658-R663.-   Thut, G., Schyns, P. G., Gross, J., 2011a. Entrainment of    perceptually relevant brain oscillations by non-invasive rhythmic    stimulation of the human brain. Front Psychology 2, 170.-   Thut, G., Veniero, D., Romei, V., Miniussi, C., Schyns, P., Gross,    J., 2011 b. Rhythmic TMS causes local entrainment of natural    oscillatory signatures. Current Biology 21, 1176-1185.-   Vallar, G., Bolognini, N., 2011. Behavioural facilitation following    brain stimula-tion: implications for neurorehabilitation.    Neuropsychological Rehabilitation 21, 618-649.-   Varela, F., Lachaux, J. P., Rodriguez, E., Martinerie, J., 2001. The    brainweb: phase synchronization and large-scale integration. Nature    Reviews Neuroscience 2, 229-239.-   Veniero, D., Brignani, D., Thut, G., Miniussi, C., 2011.    Alpha-generation as basic response-signature to transcranial    magnetic stimulation (TMS) targeting the human resting motor cortex:    a TMS/EEG co-registration study. Psychophysiology 48, 1381-1389.-   Walsh, V., Cowey, A., 2000. Transcranial magnetic stimulation and    cognitive neuroscience. Nature Reviews Neuroscience 1, 73-79.-   Walsh, V., Ellison, A., Battelli, L., Cowey, A., 1998. Task-specific    impairments and enhancements induced by magnetic stimulation of    human visual area V5. Proceedings: Biological Sciences 265, 537-543.-   Walsh, V., Pascual-Leone, A., 2003. Transcranial Magnetic    Stimulation: A Neurochronometrics of Mind. MIT Press, Cambridge,    Mass.-   Walsh, V., Rushworth, M., 1999. A primer of magnetic stimulation as    a tool for neuropsychology. Neuropsychologia 37, 125-135.-   Ward, L. M., Doesburg, S. M., Kitajo, K., MacLean, S. E.,    Roggeveen, A. B., 2006. Neural synchrony in stochastic resonance,    attention, and consciousness. Canadian J. Experimental Psychology    60, 319-326.-   Wassermann, E. M., Epstein, C., Ziemann, U., Walsh, V., Paus, T.,    Lisanby, S., 2008. Handbook of Transcranial Stimulation. Oxford    University Press, Oxford, UK.-   Waterston, M. L., Pack, C. C., 2010. Improved discrimination of    visual stimuli following repetitive transcranial magnetic    stimulation. PLoS ONE 5, e10354.-   Wu, S., Amari, S., Nakahara, H., 2002. Population coding and    decoding in a neural field: a computational study. Neural    Computation 14, 999-1026.-   Zaehle, T., Rach, S., Hermann, C. S., 2010. Transcranial alternating    current stimulation enhances individual alpha activity in human EEG.    PLoS ONE 5, e13766.-   Michael A. Nitsche, and Armin Kibele. “Noninvasive brain stimulation    and neural entrainment enhance athletic performance—a review.” J.    Cognitive Enhancement 1.1 (2017): 73-79, discusses that non-invasive    brain stimulation (NIBS) bypasses the correlative approaches of    other imaging techniques, making it possible to establish a causal    relationship between cognitive processes and the functioning of    specific brain areas. NIBS can provide information about where a    particular process occurs. NIBS offers the opportunity to study    brain mechanisms beyond process localization, providing information    about when activity in a given brain region is involved in a    cognitive process, and even how it is involved. When using NIBS to    explore cognitive processes, it is important to understand not only    how NIBS functions but also the functioning of the neural structures    themselves. Non-invasive brain stimulation (NIBS) methods, which    include transcranial magnetic stimulation (TMS) and transcranial    electric stimulation (tES), are used in cognitive neuroscience to    induce transient changes in brain activity and thereby alter the    behavior of the subject. The application of NIBS aims at    establishing the role of a given cortical area in an ongoing    specific motor, perceptual or cognitive process (Hallett, 2000;    Walsh and Cowey, 2000). Physically, NIBS techniques affect neuronal    states through different mechanisms. In TMS, a solenoid (coil) is    used to deliver a strong and transient magnetic field, or “pulse,”    to induce a transitory electric current at the cortical surface    beneath the coil. (US 2004078056) The pulse causes the rapid and    above-threshold depolarization of cell membranes affected by the    current (Barker et al., 1985, 1987), followed by the transsynaptic    depolarization or hyperpolarization of interconnected neurons.    Therefore, TMS induces a current that elicits action potentials in    neurons. A complex set of coils can deliver a complex 3D excitation    field. By contrast, in tES techniques, the stimulation involves the    application of weak electrical currents directly to the scalp    through a pair of electrodes (Nitsche and Paulus, 2000; Priori et    al., 1998). As a result, tES induces a subthreshold polarization of    cortical neurons that is too weak to generate an action potential.    However, by changing the intrinsic neuronal excitability, tES can    induce changes in the resting membrane potential and the    postsynaptic activity of cortical neurons. This, in turn, can alter    the spontaneous firing rate of neurons and modulate their response    to afferent signals (Bindman et al., 1962, 1964, 1979; Creutzfeldt    et al., 1962), leading to changes in synaptic efficacy. The typical    application of NIBS involves different types of protocols: TMS can    be delivered as a single pulse (spTMS) at a precise time, as pairs    of pulses separated by a variable interval, or as a series of    stimuli in conventional or patterned protocols of repetitive TMS    (rTMS) (for a complete classification see Rossi et al., 2009). In    tES, different protocols are established by the electrical current    used and by its polarity, which can be direct (anodal or cathodal    transcranial direct current stimulation: tDCS), high-definition    transcranial direct current stimulation (HD-tDCS), oscillating    transcranial direct current stimulation (osc-tDCS), alternating at a    fix frequency (transcranial alternating current stimulation: tACS)    transcranial pulsed current stimulation (tPCS) (electrosleep), or at    random frequencies (transcranial random noise stimulation: tRNS)    (Nitsche et al., 2008; Paulus, 2011).

NIBS also includes brain entrainment using light stimulation and soundstimulation. The latter can be binaural beats (BB) or isochronic tones.

In general, the final effects of NIBS on the central nervous systemdepend on a lengthy list of parameters (e.g., frequency, temporalcharacteristics, intensity, geometric configuration of thecoil/electrode, i.e., “montage,” current direction), when it isdelivered before (off-line) or during (on-line) the task as part of theexperimental procedure (e.g., Jacobson et al., 2011; Nitsche and Paulus,2011; Sandrini et al., 2011). In addition, these factors interact withseveral variables related to the brain anatomy and morphology (e.g.,brain size, properties of the brain tissue and its location, Radman etal., 2007), as well as physiological (e.g., gender and age, Landi andRossini, 2010; Lang et al., 2011; Ridding and Ziemann, 2010) andcognitive (e.g., Miniussi et al., 2010; Silvanto et al., 2008; Walsh etal., 1998) states of the stimulated area/subject.

Transcranial Direct Current Stimulation (tDCS)

Cranial electrotherapy stimulation (CES) is a form of non-invasive brainstimulation that applies a small, pulsed electric current across aperson's head to treat a variety of conditions such as anxiety,depression and insomnia. See,en.wikipedia.org/wiki/Cranial_electrotherapy_stimulation. Transcranialdirect current stimulation (tDCS, HD-tDCS, osc-tDCS, tPCS) is a form ofneurostimulation that uses constant, low current delivered to the brainarea of interest via electrodes on the scalp. It was originallydeveloped to help patients with brain injuries or psychiatric conditionslike major depressive disorder. tDCS appears to have some potential fortreating depression. See,en.wikipedia.org/wiki/Transcranial_direct-current_stimulation.

The hypotheses concerning the application of tDCS in cognition are verysimilar to those of TMS, with the exception that tDCS was neverconsidered a virtual lesion method. tDCS can increase or decreasecortical excitability in the stimulated brain regions and facilitate orinhibit behavior accordingly. TES does not induce action potentials butinstead modulates the neuronal response threshold so that it can bedefined as subthreshold stimulation.

tDCS is being studied for acceleration of learning. The mild electricalshock (usually, a 2-milliamp current) is used to depolarize the neuronalmembranes, making the cells more excitable and responsive to inputs.Weisend, Experimental Brain Research, vol 213, p 9 (DARPA) showed thattDCS accelerates the formation of new neural pathways during the timethat someone practices a skill. tDCS appears to bring about the flowstate. The movements of the subjects become more automatic; they reportcalm, focused concentration, and their performance improves immediately.(See Adee, Sally, “Zap your brain into the zone: Fast track to purefocus”, New Scientist, No. 2850, Feb. 1, 2012,www.newscientist.com/article/mg21328501-600-zap-your-brain-into-the-zone-fast-track-to-pure-focus/).

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 7,856,264; 8,706,241;8,725,669; 9,037,224; 9,042,201; 9,095,266; 9,248,286; 9,349,178;9,629,568; 9,693,725; 9,713,433; 20040195512; 20070179534; 20110092882;20110311021; 20120165696; 20140142654; 20140200432; 20140211593;20140316243; 20140347265; 20150099946; 20150174418; 20150257700;20150327813; 20150343242; 20150351655; 20160000354; 20160038049;20160113569; 20160144175; 20160148371; 20160148372; 20160180042;20160213276; 20160228702; and 20160235323.

Reinhart, Robert M G. “Disruption and rescue of interareal theta phasecoupling and adaptive behavior.” Proceedings of the National Academy ofSciences (2017): provide evidence for a causal relation betweeninterareal theta phase synchronization in frontal cortex and multiplecomponents of adaptive human behavior. Reinhart's results support theidea that the precise timing of rhythmic population activity spatiallydistributed in frontal cortex conveys information to direct behavior.Given prior work showing that phase synchronization can change spiketime-dependent plasticity, together with Reihart's findings showingstimulation effects on neural activity and behavior can outlast a 20-minperiod of electrical stimulation, it is reasonable to suppose that theexternally modulated interareal coupling changed behavior by causingneuroplastic modifications in functional connectivity. Reinhart suggeststhat we may be able to noninvasively intervene in the temporal couplingof distant rhythmic activity in the human brain to optimize (or impede)the postsynaptic effect of spikes from one area on the other, improving(or impairing) the cross-area communication necessary for cognitiveaction control and leaning. Moreover, these neuroplastic alterations infunctional connectivity were induced with a 0° phase, suggesting thatinducing synchronization does not require a meticulous accounting of thecommunication delay between regions such as medial frontal cortex (MFC)and lateral prefrontal cortex (IPFC) to effectively modify behavior andleaning. This conforms to work showing that despite long axonalconduction delays between distant brain areas, theta phasesynchronizations at 0° phase lag can occur between these regions andunderlie meaningful functions of cognition and action. It is alsopossible that a third subcortical or posterior region with a nonzerotime lag interacted with these two frontal areas to drive changes ingoal-directed behavior.

-   Alexander W H & Brown J W (2011) Medial prefrontal cortex as an    action-outcome predictor. Nature Neuroscience 14(10):1338-1344.-   Alexander W H & Brown J W (2015) Hierarchical error representation:    A computational model of anterior cingulate and dorsolateral    prefrontal cortex. Neural Computation 27:2354-2410.-   Anguera J A, et al. (2013) Video game training enhances cognitive    control in older adults. Nature 501:97-101.-   Aron A R, Fletcher P C, Bullmore E T, Sahakian B J, Robbins T    W (2003) Stop-signal inhibition disrupted by damage to right    inferior frontal gyrus in humans. Nat Neurosci 6:115-116.-   Au J, et al. (2015) Improving fluid intelligence with training on    working memory: a meta-analysis. Psychonomic Bulletin & Review    22:366-377.-   Bellman R, Kalaba R (1959) A mathematical theory of adaptive control    processes. Proc Natl Acad Sci USA 45:1288-1290.-   Bibbig A, Traub R D, Whittington M A (2002) Long-range    synchronization of gamma and beta oscillations and the plasticity of    excitatory and inhibitory synapses: A network model. J Neurophysiol    88:1634-1654.-   Botvinick M M (2012) Hierarchical reinforcement learning and    decision making. Current Opinion in Neurobiology 22(6):956-962.-   Botvinick M M, Braver T S, Barch D M, Carter C S, & Cohen J D (2001)    Conflict monitoring and cognitive control. Psychological Review    108(3):624-652.-   Bryck R L & Fisher P A (2012) Training the brain: practical    applications of neural plasticity from the intersection of cognitive    neuroscience, developmental psychology, and prevention science.    American Psychologist 67:87-100.-   Cavanagh J F, Cohen M X, & Allen J J (2009) Prelude to and    resolution of an error EEG phase synchrony reveals cognitive control    dynamics during action monitoring. Journal of Neuroscience    29(1):98-105.-   Cavanagh J F, Frank M J (2014) Frontal theta as a mechanism for    cognitive control. Trends Cogn Sci 18:414-421.-   Christie G J, Tata M S (2009) Right frontal cortex generates    reward-related theta-band oscillatory activity. Neuroimage    48:415-422.-   Cohen M X, Wilmes K, Vijver Iv (2011) Cortical electrophysiological    network dynamics of feedback learning. Trends Cogn Sci 15:558-566.-   Corbett A, et al. (2015) The effect of an online cognitive training    package in healthy older adults: An online randomized controlled    trial. J Am Med Dir Assoc 16:990-997.-   Dale A M & Sereno M I (1993) Improved localization of cortical    activity by combining EEG and MEG with MRI cortical surface    reconstruction: A linear approach. Journal of Cognitive Neuroscience    5:162-176.-   Dalley J W, Robbins T W (2017) Fractionating impulsivity:    Neuropsychiatric implications. Nat Rev Neurosci 18:158-171.-   Delorme A & Makeig S (2004) EEGLAB: An open source toolbox for    analysis of single-trial EEG dynamics including independent    component analysis. Journal of Neuroscience Methods 134(1):9-21.-   Diamond A & Lee K (2011) Interventions and programs demonstrated to    aid executive function development in children 4-12 years of age.    Science 333:959964.-   Engel A K, Fries P, Singer W (2001) Dynamic predictions:    Oscillations and synchrony in top-down processing. Nat Rev Neurosci    2:704-716.-   Fairclough S H & Houston K (2004) A metabolic measure of mental    effort Biological Psychology 66:177-190.-   Fell J, Axmacher N (2011) The role of phase synchronization in    memory processes. Nat Rev Neurosci 12:105-118.-   Fitzgerald K D, et al. (2005) Error-related hyperactivity of the    anterior cingulate cortex in obsessive-compulsive disorder. Biol    Psychiatry 57:287-294.-   Foti D, Weinberg A, Dien J, Hajcak G (2011) Event-related potential    activity in the basal ganglia differentiates rewards from    nonrewards: Temporospatial principal components analysis and source    localization of the feedback negativity. Hum Brain Mapp    32:2207-2216.-   Fuchs M, Drenckhahn R, Wischmann H A, & Wagner M (1998) An improved    boundary element method for realistic volume-conductor modeling.    IEEE Trans Biomed Eng 45(8):980-997.-   Gailliot M T & Baumeister R F (2007) The physiology of willpower    linking blood glucose to self-control. Personality and Social    Psychology Review 11(4):303-327.-   Gandiga P, Hummel F, & Cohen L (2006) Transcranial DC stimulation    (tDCS): A tool for double-blind sham-controlled clinical studies in    brain stimulation. Clinical Neurophysiology 117(4):845-850.-   Gregoriou G G, Gotts S J, Zhou H, Desimone R (2009) High-frequency,    long-range coupling between prefrontal and visual cortex during    attention. Science 324: 1207-1210.-   Hillman C H, Erickson K I, & Kramer A F (2008) Be smart, exercise    your heart exercise effects on brain and cognition. Nature Reviews    Neuroscience 9(1):5865.-   Holroyd C B & Yeung N (2012) Motivation of extended behaviors by    anterior cingulate cortex. Trends in Cognitive Sciences 16:122-128.-   Inzlicht M, Schmeichel B J, & Macrae C N (2014) Why self-control    seems (but may not be) limited. Trends in Cognitive Sciences    18(3):127-133.-   Jennings J R & Wood C C (1976) The e-adjustment procedure for    repeated measures analyses of variance. Psychophysiology 13:277-278.-   Kanai R, Chaieb L, Antal A, Walsh V, & Paulus W (2008)    Frequency-dependent electrical stimulation of the visual cortex.    Current Biology 18(23):1839-1843.-   Kayser J & Tenke C E (2006) Principal components analysis of    Laplacian waveforms as a generic method for identifying    estimates: II. Adequacy of low-density estimates. Clinical    Neurophysiology 117:369-380.-   Kramer A F & Erickson K I (2007) Capitalizing on cortical    plasticity: influence of physical activity on cognition and brain    function. Trends in Cognitive Sciences 11:342-348.-   Kurland J, Baldwin K, Tauer C (2010) Treatment-induced    neuroplasticity following intensive naming therapy in a case of    chronic Wemicke's aphasia. Aphasiology 24: 737-751.-   Lachaux J P, Rodriguez E, Martinerie J, & Varela F J (1999)    Measuring phase synchrony in brain signals. Human Brain Mapping    8:194-208.-   Lennie P (2003) The cost of cortical computation. Current Biology    13:493-497.-   Luft C D B, Note G, & Bhattacharya J (2013) High-learners present    larger midfrontal theta power and connectivity in response to    incorrect performance feedback. Journal of Neuroscience    33(5):2029-2038.-   Luft C D B, Note G, Bhattacharya J (2013) High-leaners present    larger mid-frontal theta power and connectivity in response to    incorrect performance feedback. J Neurosci 33:2029-2038.-   Marco-Pallares J, et al. (2008) Human oscillatory activity    associated to reward processing in a gambling task. Neuropsychologia    46:241-248.-   Marcora S M, Staiano W, & Manning V (2009) Mental fatigue impairs    physical performance in humans. Journal of Applied Physiology    106:857-864.-   Miltner W H R, Braun C H, & Coles M G H (1997) Event-related brain    potentials following incorrect feedback in a time-estimation task:    evidence for a “generic” neural system for error detection. Journal    of Cognitive Neuroscience 9:788-798.-   Noury N, Hipp J F, Siegel M (2016) Physiological processes    non-linearly affect electrophysiological recordings during    transcranial electric stimulation. Neuroimage 140: 99-109.-   Oostenveld R, Fries P, Maris E, & Schoffelen J M (2011) FieldTrip:    Open source software for advanced analysis of MEG, EEG, and invasive    electrophysiological data. Computational Intelligence and    Neuroscience 2011:1-9.-   Owen A M, et al. (2010) Putting brain training to the test Nature    465:775-778.-   Pascual-Marqui R D (2002) Standardized low-resolution brain    electromagnetic tomography (sLORETA): technical details. Methods &    Findings in Experimental & Clinical Pharmacology 24:5-12.-   Paulus W (2010) On the difficulties of separating retinal from    cortical origins of phosphenes when using transcranial alternating    current stimulation (tACS). Clinical Neurophysiology 121:987-991.-   Poreisz C, Boros K, Antal A, & Paulus W (2007) Safety aspects of    transcranial direct current stimulation concerning healthy subjects    and patients. Brain Research Bulletin 72(4-6):208-214.-   Raichle M E & Mintun M A (2006) Brain work and brain imaging. Annual    Review of Neuroscience 29:449-476.-   Reinhart R M G & Woodman G F (2014) Causal control of medial-frontal    cortex governs electrophysiological and behavioral indices of    performance monitoring and learning. Journal of Neuroscience    34(12):4214-4227.-   Reinhart R M G & Woodman G F (2015) Enhancing long-term memory with    stimulation tunes visual attention in one trial. Proceedings of the    National Academy of Sciences of the USA 112(2):625-630.-   Reinhart R M G, Cosman J D, Fukuda K, & Woodman G F (2017) Using    transcranial direct-current stimulation (tDCS) to understand    cognitive processing. Attention, Perception & Psychophysics    79(1):3-23.-   Reinhart R M G, Woodman G F (2014) Oscillatory coupling reveals the    dynamic reorganization of large-scale neural networks as cognitive    demands change. J Cogn Neurosci 26:175-188.-   Reinhart R M G, Xiao W, McClenahan L, & Woodman G F (2016)    Electrical stimulation of visual cortex can immediately improve    spatial vision. Current Biology 25(14):1867-1872.-   Reinhart R M G, Zhu J, Park S, & Woodman G F (2015) Medial-frontal    stimulation enhances learning in schizophrenia by restoring    prediction-error signaling. Journal of Neuroscience    35(35):12232-12240.-   Reinhart R M G, Zhu J, Park S, & Woodman G F (2015) Synchronizing    theta oscillations with direct-current stimulation strengthens    adaptive control in the human brain. Proceedings of the National    Academy of Sciences of the USA 112(30):9448-9453.-   Ridderinkhof K R, Ullsperger M, Crone E A, & Nieuwenhuis S (2004)    The role of the medial frontal cortex in cognitive control. Science    306:443-447.-   Salinas E, Sejnowski T J (2001) Correlated neuronal activity and the    flow of neural information. Nat Rev Neurosci 2:539-550.-   Schnitzer A, Gross J (2005) Normal and pathological oscillatory    communication in the brain. Nat Rev Neurosci 6:285-296.-   Schutter D J & Hortensius R (2010) Retinal origin of phosphenes to    transcranial alternating current stimulation. Clinical    Neurophysiology 121(7):1080-1084.-   Shallice T, Gazzaniga M S (2004) The fractionation of supervisory    control. The Cognitive Neuroscience (MIT Press, Cambridge, Mass.),    pp 943-956.-   Shenhav A, Botvinick M M, & Cohen J D (2013) The expected value of    control: An integrative theory of anterior cingulate cortex    function. Neuron 79:217-240.-   Shenhav A, Cohen J D, & Botvinick M M (2016) Dorsal anterior    cingulate cortex and the value of control. Nature Neuroscience    19:1286-1291.-   Siegel M, Donner T H, Engel A K (2012) Spectral fingerprints of    large-scale neuronal interactions. Nat Rev Neurosci 13:121-134.-   Srinivasan R, Winter W R, Ding J, & Nunez P L (2007) EEG and MEG    coherence: measures of functional connectivity at distinct spatial    scales of neocortical dynamics. Journal of Neuroscience Methods    166(1):41-52.-   Tang Y, et al. (2010) Short term mental training induces    white-matter changes in the anterior cingulate. Proceedings of the    National Academy of Sciences 107:16649-16652.-   Tang Y Y, et al. (2009) Central and autonomic nervous system    interaction is altered by short term meditation. Proceedings of the    National Academy of Sciences 106:8865-8870.-   Thrane G, Friborg O, Anke A, Indredavik B (2014) A meta-analysis of    constraint-induced movement therapy after stroke. J Rehabil Med    46:833-842.-   Uhlhaas P J, Singer W (2006) Neural synchrony in brain disorders:    Relevance for cognitive dysfunctions and pathophysiology. Neuron    52:155-168.-   Uhlhaas P J, Singer W (2010) Abnormal neural oscillations and    synchrony in schizophrenia. Nat Rev Neurosci 11:100-113.-   van de Vijver I, Ridderinkhof K R, & Cohen M X (2011) Frontal    oscillatory dynamics predict feedback learning and action adjustment    Journal of Cognitive Neuroscience 23:4106-4121.-   van Driel J, Ridderinkhof K R, & Cohen M X (2012) Not all errors are    alike: Theta and alpha EEG dynamics relate to differences in    error-processing dynamics. Journal of Neuroscience    32(47):16795-16806.-   van Meel C S, Heslenfeld D J, Oosterlaan J, Sergeant J A (2007)    Adaptive control deficits in attention-deficit/hyperactivity    disorder (ADHD): The role of error processing. Psychiatry Res    151:211-220.-   Varela F, Lachaux J P, Rodriguez E, Martinerie J (2001) The    brainweb: Phase synchronization and large-scale integration. Nat Rev    Neurosci 2:229-239.-   Velligan D I, Ritch J L, Sui D, DiCocco M, Huntzinger C D (2002)    Frontal systems behavior scale in schizophrenia: Relationships with    psychiatic symptomatology, cognition and adaptive function.    Psychiatry Res 113:227-236.-   Vicente R, Gollo L L, Mirasso C R, Fischer I, Pipa G (2008)    Dynamical relaying can yield zero time lag neuronal synchrony    despite long conduction delays. Proc Natl Acad Sci USA    105:17157-17162.-   Wagner M, Fuchs M, & Kastner J (2007) SWAR M: sLORETA-weighted    accurate minimum norm inverse solutions. International Congress    Series 1300:185-188.-   Wang X J (2010) Neurophysiological and computational principles of    cortical rhythms in cognition. Physiol Rev 90:1195-1268.-   Wolpert D M, Diedrichsen J, & Flanagan J R (2011) Principles of    sensorimotor learning. Nature Reviews Neuroscience 12:739-751.-   Xue S, Tang Y Y, Tang R, & Posner M I (2014) Short-term meditation    induces changes in brain resting EEG theta networks. Brain &    Cognition 87:1-6-   Zatorre R J, Fields R D, & Johansen-Berg H (2012) Plasticity in gray    and white: neuroimaging changes in brain structure during learning.    Nature Neuroscience 15(4):528-536. See, Daniel Stevenson. “Intro to    Transcranial Direct Current Stimulation (tDCS)” (Mar. 26, 2017)    (www.slideshare.net/DanielStevenson27/intro-to-transcranial-direct-curent-stimulation-tdcs).

High-Definition-tDCS

High-Definition transcranial Direct Current Stimulation (HD-tDCS) wasinvented by Dr. Marom Bikson's group at The City College of New Yorkwith the introduction of the 4×1 HD-tDCS montage. The 4×1 HD-tDCSmontage allows precise targeting of cortical structures. The region ofcurrent flow is circumscribed by the area of the 4× ring, such thatdecreasing ring radius increases focality. 4×1 HD-tDCS allows forunifocal stimulation, meaning the polarity of the center 1× electrodewill determine the direction of neuromodulation under the ring. This isin contrast to conventional tDCS where the need for one anode and onecathode always produces bidirectional modulation (even when anextra-cephalic electrode is used). 4×1 HD-tDCS thus provides the abilitynot only to select a cortical brain region to target, but to modulatethe excitability of that brain region with a designed polarity withouthaving to consider return counter-electrode flow.

Osc-tDCS

Oscillating transcranial direct current stimulation (osc-tDCS) is a tDCSwherein the amplitude of the current is modulated with a sinusoidwaveform of a certain frequency. Osc-tDCS modulates the spontaneousbrain activity in a frequency-specific manner. Osc-tDCS mainly affectsbrain oscillatory activity. Anodal oscillatory stimulation at 0.75 Hz(slow osc-tDCS) in frontal areas during sleep stage 2 of a diumal nap orduring nocturnal sleep can induce a frequency-specific enhancement ofSlow-Wave Activity (SWA, 0.5-4 Hz) during sleep. The enhancement innormal subjects of SWA induced by osc-tDCS at 0.75 Hz during sleepsignificantly improves performance in a memory task after sleep. SeeBergmann T O, Groppa S, Seeger M, Mlle M, Marshall L, Siebner H R.“Acute changes in motor cortical excitability during slow oscillatoryand constant anodal transcranial direct current stimulation.” JNeurophysiol. 2009 October 102(4):2303-11. Marshal L, Helgadóttir H,Mölle M, Born J. “Boosing slow oscillations during sleep potentatesmemory.” Nature. 2006 Nov. 30; 444(7119):610-3. Marshall L, Kirov R,Brade J, Mölle M, Born J

“Transcranial electrical currents to probe EEG brain rhythms and memoryconsolidation during sleep in humans.” PLoS One. 2011 Feb. 14;6(2):e16905.

Transcranial Alternative Current Stimulation (tACS)

Transcranial alternating current stimulation (tACS) is a noninvasivemeans by which alternating electrical current applied through the skinand skull entrains in a frequency-specific fashion the neuraloscillations of the underlying brain. See,en.wikipedia.org/wiki/Transcranial_alternating_current_stimulation

U.S. Pub. App. No. 20170197081 discloses transdermal electricalstimulation of nerves to modify or induce a cognitive state usingtransdermal electrical stimulation (TES).

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 6,804,558; 7,149,773;7,181,505; 7,278,966; 9,042,201; 9,629,568; 9,713,433; 20010051787;20020013613; 20020052539; 20020082665; 20050171410; 20140211593;20140316243; 20150174418; 20150343242; 20160000354; 20160038049;20160106513; 20160213276; 20160228702; 20160232330; 20160235323; and20170113056.

Transcranial Random Noise Stimulation (tRNS)

Transcranial random noise stimulation (tRNS) is a non-invasive brainstimulation technique and a form of transcranial electrical stimulation(tES). See, en.wikipedia.org/wiki/Transcranial_random_noise_stimulation;U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 9,198,733; 9,713,433;20140316243; 20160038049; and 20160213276.

Transcranial Pulsed Current Stimulation (tPCS)

The stimulus may comprise transcranial pulsed current stimulation(tPCS). See:

-   Shapour Jaberzadeh, Andisheh Bastani, Maryam Zoghi, “Anodal    transcranial pulsed current stimulation: A novel technique to    enhance corticospinal excitability,” Clin. Neurophysiology, Volume    125, Issue 2, February 2014, Pages 344-351,    doi.org/10.1016j.clinph.2013.08.025;-   earthpulse.net/tpcs-transcranial-pulsed-current-stimulation/;    help.foc.us/article/16-tpcs-transcranial-pulsed-current-stimulation.

Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a method in which a changingmagnetic field is used to cause electric current to flow in a smallregion of the brain via electromagnetic induction. During a TMSprocedure, a magnetic field generator, or “coil”, is placed near thehead of the person receiving the treatment. The coil is connected to apulse generator, or stimulator, that delivers a changing electriccurrent to the coil. TMS is used diagnostically to measure theconnection between the central nervous system and skeletal muscle toevaluate damage in a wide variety of disease states, including stroke,multiple sclerosis, amyotrophic lateral sclerosis, movement disorders,and motor neuron diseases. Evidence is available suggesting that TMS isuseful in treating neuropathic pain, major depressive disorder, andother conditions.

See, en.wikipedia.org/wiki/Transcranial_magnetic_stimulation,

See U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 4,296,756; 4,367,527;5,069,218; 5,088,497; 5,359,363; 5,384,588; 5,459,536; 5,711,305;5,877,801; 5,891,131; 5,954,662; 5,971,923; 6,188,924; 6,259,399;6,487,441; 6,603,502; 7,714,936; 7,844,324; 7,856,264; 8,221,330;8,655,817; 8,706,241; 8,725,669; 8,914,115; 9,037,224; 9,042,201;9,095,266; 9,149,195; 9,248,286; 9,265,458; 9,414,776; 9,445,713;9,713,433; 20020097332; 20040088732; 20070179534; 20070249949;20080194981; 20090006001; 20110004412; 20110007129; 20110087127;20110092882; 20110119212; 20110137371; 20120165696; 20120296569;20130339043; 20140142654; 20140163328; 20140200432; 20140211593;20140257047; 20140279746; 20140316243; 20140350369; 20150065803;20150099946; 20150148617; 20150174418; 20150257700; 20150327813;20150343242; 20150351655; 20160038049; 20160140306; 20160144175;20160213276; 20160235323; 20160284082; 20160306942; 20160317077;20170084175; and 20170113056.

Pulsed Electromagnetic Field (PEMF)

Pulsed electromagnetic field (PEMF) when applied to the brain isreferred to as Transcranial magnetic stimulation, and has been FDAapproved since 2008 for use in people who failed to respond toantidepressants. Weak magnetic stimulation of the brain is often calledtranscranial pulsed electromagnetic field (tPEMF) therapy. See,en.wikipedia.org/wiki/Pulsed_electromagnetic_field_therapy,

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 7,280,861; 8,343,027;8,415,123; 8,430,805; 8,435,166; 8,571,642; 8,657,732; 8,775,340;8,961,385; 8,968,172; 9,002,477; 9,005,102; 9,278,231; 9,320,913;9,339,641; 9,387,338; 9,415,233; 9,427,598; 9,433,797; 9,440,089;9,610,459; 9,630,004; 9,656,096; 20030181791; 20060129022; 20100057655;20100197993; 20120101544; 20120116149; 20120143285; 20120253101;20130013339; 20140213843; 20140213844; 20140221726; 20140228620;20140303425; 20160235983; 20170087367; and 20170165496.

Deep Brain Stimulation (DBS)

Deep brain stimulation (DBS) is a neurosurgical procedure involving theimplantation of a medical device called a neurostimulator (sometimesreferred to as a ‘brain pacemaker’), which sends electrical impulses,through implanted electrodes, to specific targets in the brain (brainnuclei) for the treatment of movement and neuropsychiatric disorders.See, en.wikipedia.org/wiki/Deep_brain_stimulation.

Transcranial Pulse Ultrasound (TPU)

Transcranial pulsed ultrasound (TPU) uses low intensity, low frequencyultrasound (LILFU) as a method to stimulate the brain. See,en.wikipedia.org/wiki/Transcranial_pulsed_ultrasound;

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 8,591,419; 8,858,440;8,903,494; 8,921,320; 9,002,458; 9,014,811; 9,036,844; 9,042,201;9,061,133; 9,233,244; 9,333,334; 9,399,126; 9,403,038; 9,440,070;9,630,029; 9,669,239; 20120259249; 20120283502; 20120289869;20130079621; 20130144192; 20130184218; 20140058219; 20140211593;20140228653; 20140249454; 20140316243; 20150080327; 20150133716;20150343242; 20160143541; 20160176053; and 20160220850.

Sensory Stimulation

Light, sound or electromagnetic fields may be used to remotely convey atemporal pattern of brainwaves. See:

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 5,293,187; 5,422,689;5,447,166; 5,491,492; 5,546,943; 5,622,168; 5,649,061; 5,720,619;5,740,812; 5,983,129; 6,050,962; 6,092,058; 6,149,586; 6,325,475;6,377,833; 6,394,963; 6,428,490; 6,482,165; 6,503,085; 6,520,921;6,522,906; 6,527,730; 6,556,695; 6,565,518; 6,652,458; 6,652,470;6,701,173; 6,726,624; 6,743,182; 6,746,409; 6,758,813; 6,843,774;6,896,655; 6,996,261; 7,037,260; 7,070,571; 7,107,090; 7,120,486;7,212,851; 7,215,994; 7,260,430; 7,269,455; 7,280,870; 7,392,079;7,407,485; 7,463,142; 7,478,108; 7,488,294; 7,515,054; 7,567,693;7,647,097; 7,740,592; 7,751,877; 7,831,305; 7,856,264; 7,881,780;7,970,734; 7,972,278; 7,974,787; 7,991,461; 8,012,107; 8,032,486;8,033,996; 8,060,194; 8,095,209; 8,209,224; 8,239,030; 8,262,714;8,320,649; 8,358,818; 8,376,965; 8,380,316; 8,386,312; 8,386,313;8,392,250; 8,392,253; 8,392,254; 8,392,255; 8,437,844; 8,464,288;8,475,371; 8,483,816; 8,494,905; 8,517,912; 8,533,042; 8,545,420;8,560,041; 8,655,428; 8,672,852; 8,682,687; 8,684,742; 8,694,157;8,706,241; 8,706,518; 8,738,395; 8,753,296; 8,762,202; 8,764,673;8,768,022; 8,788,030; 8,790,255; 8,790,297; 8,821,376; 8,838,247;8,864,310; 8,872,640; 8,888,723; 8,915,871; 8,938,289; 8,938,301;8,942,813; 8,955,010; 8,955,974; 8,958,882; 8,964,298; 8,971,936;8,989,835; 8,992,230; 8,998,828; 9,004,687; 9,060,671; 9,101,279;9,135,221; 9,142,145; 9,165,472; 9,173,582; 9,179,855; 9,208,558;9,215,978; 9,232,984; 9,241,665; 9,242,067; 9,254,099; 9,271,660;9,275,191; 9,282,927; 9,292,858; 9,292,920; 9,320,450; 9,326,705;9,330,206; 9,357,941; 9,396,669; 9,398,873; 9,414,780; 9,414,907;9,424,761; 9,445,739; 9,445,763; 9,451,303; 9,451,899; 9,454,646;9,462,977; 9,468,541; 9,483,117; 9,492,120; 9,504,420; 9,504,788;9,526,419; 9,541,383; 9,545,221; 9,545,222; 9,545,225; 9,560,967;9,560,984; 9,563,740; 9,582,072; 9,596,224; 9,615,746; 9,622,702;9,622,703; 9,626,756; 9,629,568; 9,642,699; 9,649,030; 9,651,368;9,655,573; 9,668,694; 9,672,302; 9,672,617; 9,682,232; 9,693,734;9,694,155; 9,704,205; 9,706,910; 9,710,788; RE44408; RE45766;20020024450; 20020103428; 20020103429; 20020112732; 20020128540;20030028081; 20030028121; 20030070685; 20030083596; 20030100844;20030120172; 20030149351; 20030158496; 20030158497; 20030171658;20040019257; 20040024287; 20040068172; 20040092809; 20040101146;20040116784; 20040143170; 20040267152; 20050010091; 20050019734;20050025704; 20050038354; 20050113713; 20050124851; 20050148828;20050228785; 20050240253; 20050245796; 20050267343; 20050267344;20050283053; 20060020184; 20060061544; 20060078183; 20060087746;20060102171; 20060129277; 20060161218; 20060189866; 20060200013;20060241718; 20060252978; 20060252979; 20070050715; 20070179534;20070191704; 20070238934; 20070273611; 20070282228; 20070299371;20080004550; 20080009772; 20080058668; 20080081963; 20080119763;20080123927; 20080132383; 20080228239; 20080234113; 20080234601;20080242521; 20080255949; 20090018419; 20090058660; 20090062698;20090076406; 20090099474; 20090112523; 20090221928; 20090267758;20090270687; 20090270688; 20090270692; 20090270693; 20090270694;20090270786; 20090281400; 20090287108; 20090297000; 20090299169;20090311655; 20090312808; 20090312817; 20090318794; 20090326604;20100004977; 20100010289; 20100010366; 20100041949; 20100069739;20100069780; 20100163027; 20100163028; 20100163035; 20100165593;20100168525; 20100168529; 20100168602; 20100268055; 20100293115;20110004412; 20110009777; 20110015515; 20110015539; 20110043759;20110054272; 20110077548; 20110092882; 20110105859; 20110130643;20110172500; 20110218456; 20110256520; 20110270074; 20110301488;20110307079; 20120004579; 20120021394; 20120036004; 20120071771;20120108909; 20120108995; 20120136274; 20120150545; 20120203130;20120262558; 20120271377; 20120310106; 20130012804; 20130046715;20130063434; 20130063550; 20130080127; 20130120246; 20130127980;20130185144; 20130189663; 20130204085; 20130211238; 20130226464;20130242262; 20130245424; 20130281759; 20130289360; 20130293844;20130308099; 20130318546; 20140058528; 20140155714; 20140171757;20140200432; 20140214335; 20140221866; 20140243608; 20140243614;20140243652; 20140276130; 20140276944; 20140288614; 20140296750;20140300532; 20140303508; 20140304773; 20140313303; 20140315169;20140316191; 20140316192; 20140316235; 20140316248; 20140323899;20140335489; 20140343408; 20140347491; 20140350353; 20140350431;20140364721; 20140378810; 20150002815; 20150003698; 20150003699;20150005640; 20150005644; 20150006186; 20150012111; 20150038869;20150045606; 20150051663; 20150099946; 20150112409; 20150120007;20150124220; 20150126845; 20150126873; 20150133812; 20150141773;20150145676; 20150154889; 20150174362; 20150196800; 20150213191;20150223731; 20150234477; 20150235088; 20150235370; 20150235441;20150235447; 20150241705; 20150241959; 20150242575; 20150242943;20150243100; 20150243105; 20150243106; 20150247723; 20150247975;20150247976; 20150248169; 20150248170; 20150248787; 20150248788;20150248789; 20150248791; 20150248792; 20150248793; 20150290453;20150290454; 20150305685; 20150306340; 20150309563; 20150313496;20150313539; 20150324692; 20150325151; 20150335288; 20150339363;20150351690; 20150366497; 20150366504; 20150366656; 20150366659;20150369864; 20150370320; 20160000354; 20160004298; 20160005320;20160007915; 20160008620; 20160012749; 20160015289; 20160022167;20160022206; 20160029946; 20160029965; 20160038069; 20160051187;20160051793; 20160066838; 20160073886; 20160077547; 20160078780;20160106950; 20160112684; 20160120436; 20160143582; 20160166219;20160167672; 20160176053; 20160180054; 20160198950; 20160199577;20160202755; 20160216760; 20160220439; 20160228640; 20160232625;20160232811; 20160235323; 20160239084; 20160248994; 20160249826;20160256108; 20160267809; 20160270656; 20160287157; 20160302711;20160306942; 20160313798; 20160317060; 20160317383; 20160324478;20160324580; 20160334866; 20160338644; 20160338825; 20160339300;20160345901; 20160357256; 20160360970; 20160363483; 20170000324;20170000325; 20170000326; 20170000329; 20170000330; 20170000331;20170000332; 20170000333; 20170000334; 20170000335; 20170000337;20170000340; 20170000341; 20170000342; 20170000343; 20170000345;20170000454; 20170000683; 20170001032; 20170006931; 20170007111;20170007115; 20170007116; 20170007122; 20170007123; 20170007165;20170007182; 20170007450; 20170007799; 20170007843; 20170010469;20170010470; 20170017083; 20170020447; 20170020454; 20170020627;20170027467; 20170027651; 20170027812; 20170031440; 20170032098;20170035344; 20170043160; 20170055900; 20170060298; 20170061034;20170071523; 20170071537; 20170071546; 20170071551; 20170080320;20170086729; 20170095157; 20170099479; 20170100540; 20170103440;20170112427; 20170112671; 20170113046; 20170113056; 20170119994;20170135597; 20170135633; 20170136264; 20170136265; 20170143249;20170143442; 20170148340; 20170156662; 20170162072; 20170164876;20170164878; 20170168568; 20170173262; 20170173326; 20170177023;20170188947; 20170202633; 20170209043; 20170209094; and 20170209737.

Light Stimulation

The functional relevance of brain oscillations in the alpha frequencyrange (7.5-12 Hz) has been repeatedly investigated through the use ofrhythmic visual stimulation. There are two hypotheses on the origin ofsteady-state visual evoked potential (SSVEP) measured in EEG duringrhythmic stimulation: entrainment of brain oscillations andsuperposition of event-related responses (ERPs). The entrainment but notthe superposition hypothesis justifies rhythmic visual stimulation as ameans to manipulate brain oscillations, because superposition assumes alinear summation of single responses, independent from ongoing brainoscillations. Participants stimulated with rhythmic flickering light ofdifferent frequencies and intensities, and entrainment was measured bycomparing the phase coupling of brain oscillations stimulated byrhythmic visual flicker with the oscillations induced by arrhythmicjittered stimulation, varying the time, stimulation frequency, andintensity conditions. Phase coupling was found to be more pronouncedwith increasing stimulation intensity as well as at stimulationfrequencies closer to each participants intrinsic frequency. Even in asingle sequence of an SSVEP, non-linear features (intermittency of phaselocking) was found that contradict the linear summation of singleresponses, as assumed by the superposition hypothesis. Thus, evidencesuggests that visual rhythmic stimulation entrains brain oscillations,validating the approach of rhythmic stimulation as a manipulation ofbrain oscillations. See, Notbohm A, Kurths J, Hermann C S, Modificationof Brain Oscillations via Rhythmic Light Stimulation Provides Evidencefor Entrainment but Not for Superposition of Event-Related Responses,Front Hum Neurosci. 2016 Feb. 3; 10:10. doi: 10.3389/fnhum.2016.00010.eCollection 2016.

It is also known that periodic visual stimulation can trigger epilepticseizures.

Cochlear Implant

A cochlear implant is a surgically implanted electronic device thatprovides a sense of sound to a person who is profoundly deaf or severelyhard of hearing in both ears. See,en.wikipedia.org/wiki/Cochlear_implant;

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 5,999,856; 6,354,299;6,427,086; 6,430,443; 6,665,562; 6,873,872; 7,359,837; 7,440,806;7,493,171; 7,610,083; 7,610,100; 7,702,387; 7,747,318; 7,765,088;7,853,321; 7,890,176; 7,917,199; 7,920,916; 7,957,806; 8,014,870;8,024,029; 8,065,017; 8,108,033; 8,108,042; 8,140,152; 8,165,687;8,175,700; 8,195,295; 8,209,018; 8,224,431; 8,315,704; 8,332,024;8,401,654; 8,433,410; 8,478,417; 8,515,541; 8,538,543; 8,560,041;8,565,864; 8,574,164; 8,577,464; 8,577,465; 8,577,466; 8,577,467;8,577,468; 8,577,472; 8,577,478; 8,588,941; 8,594,800; 8,644,946;8,644,957; 8,652,187; 8,676,325; 8,696,724; 8,700,183; 8,718,776;8,768,446; 8,768,477; 8,788,057; 8,798,728; 8,798,773; 8,812,126;8,864,806; 8,868,189; 8,929,999; 8,968,376; 8,989,868; 8,996,120;9,002,471; 9,044,612; 9,061,132; 9,061,151; 9,095,713; 9,135,400;9,186,503; 9,235,685; 9,242,067; 9,248,290; 9,248,291; 9,259,177;9,302,093; 9,314,613; 9,327,069; 9,352,145; 9,352,152; 9,358,392;9,358,393; 9,403,009; 9,409,013; 9,415,215; 9,415,216; 9,421,372;9,432,777; 9,501,829; 9,526,902; 9,533,144; 9,545,510; 9,550,064;9,561,380; 9,578,425; 9,592,389; 9,604,067; 9,616,227; 9,643,017;9,649,493; 9,674,621; 9,682,232; 9,743,197; 9,744,358; 20010014818;20010029391; 20020099412; 20030114886; 20040073273; 20050149157;20050182389; 20050182450; 20050182467; 20050182468; 20050182469;20050187600; 20050192647; 20050209664; 20050209665; 20050209666;20050228451; 20050240229; 20060064140; 20060094970; 20060094971;20060094972; 20060095091; 20060095092; 20060161217; 20060173259;20060178709; 20060195039; 20060206165; 20060235484; 20060235489;20060247728; 20060282123; 20060287691; 20070038264; 20070049988;20070156180; 20070198063; 20070213785; 20070244407; 20070255155;20070255531; 20080049376; 20080140149; 20080161886; 20080208280;20080235469; 20080249589; 20090163980; 20090163981; 20090243756;20090259277; 20090270944; 20090280153; 20100030287; 20100100164;20100198282; 20100217341; 20100231327; 20100241195; 20100268055;20100268288; 20100318160; 20110004283; 20110060382; 20110166471;20110295344; 20110295345; 20110295346; 20110295347; 20120035698;20120116179; 20120116741; 20120150255; 20120245655; 20120262250;20120265270; 20130165996; 20130197944; 20130235550; 20140032512;20140098981; 20140200623; 20140249608; 20140275847; 20140330357;20140350634; 20150018699; 20150045607; 20150051668; 20150065831;20150066124; 20150080674; 20150328455; 20150374986; 20150374987;20160067485; 20160243362; 20160261962; 20170056655; 20170087354;20170087355; 20170087356; 20170113046; 20170117866; 20170135633; and20170182312.

Vagus Nerve Stimulation

Vagus nerve stimulation (VNS) is a medical treatment that involvesdelivering electrical impulses to the vagus nerve. It is used as anadjunctive treatment for certain types of intractable epilepsy andtreatment-resistant depression. See,en.wikipedia.org/wiki/Vagus_nerve_stimulation;

See, U.S. Patent Nos. and Pub. Pat. Nos. U.S. Pat. Nos. 5,215,086;5,231,988; 5,299,569; 5,335,657; 5,571,150; 5,928,272; 5,995,868;6,104,956; 6,167,311; 6,205,359; 6,208,902; 6,248,126; 6,269,270;6,339,725; 6,341,236; 6,356,788; 6,366,814; 6,418,344; 6,497,699;6,549,804; 6,556,868; 6,560,486; 6,587,727; 6,591,137; 6,597,954;6,609,030; 6,622,047; 6,665,562; 6,671,556; 6,684,105; 6,708,064;6,735,475; 6,782,292; 6,788,975; 6,873,872; 6,879,859; 6,882,881;6,920,357; 6,961,618; 7,003,352; 7,151,961; 7,155,279; 7,167,751;7,177,678; 7,203,548; 7,209,787; 7,228,167; 7,231,254; 7,242,984;7,277,758; 7,292,890; 7,313,442; 7,324,851; 7,346,395; 7,366,571;7,386,347; 7,389,144; 7,403,820; 7,418,290; 7,422,555; 7,444,184;7,454,245; 7,457,665; 7,463,927; 7,486,986; 7,493,172; 7,499,752;7,561,918; 7,620,455; 7,623,927; 7,623,928; 7,630,757; 7,634,317;7,643,881; 7,653,433; 7,657,316; 7,676,263; 7,680,526; 7,684,858;7,706,871; 7,711,432; 7,734,355; 7,736,382; 7,747,325; 7,747,326;7,769,461; 7,783,362; 7,801,601; 7,805,203; 7,840,280; 7,848,803;7,853,321; 7,853,329; 7,860,548; 7,860,570; 7,865,244; 7,869,867;7,869,884; 7,869,885; 7,890,185; 7,894,903; 7,899,539; 7,904,134;7,904,151; 7,904,175; 7,908,008; 7,920,915; 7,925,353; 7,945,316;7,957,796; 7,962,214; 7,962,219; 7,962,220; 7,974,688; 7,974,693;7,974,697; 7,974,701; 7,996,079; 8,000,788; 8,027,730; 8,036,745;8,041,418; 8,041,419; 8,046,076; 8,064,994; 8,068,911; 8,097,926;8,108,038; 8,112,148; 8,112,153; 8,116,883; 8,150,508; 8,150,524;8,160,696; 8,172,759; 8,180,601; 8,190,251; 8,190,264; 8,204,603;8,209,009; 8,209,019; 8,214,035; 8,219,188; 8,224,444; 8,224,451;8,229,559; 8,239,028; 8,260,426; 8,280,505; 8,306,627; 8,315,703;8,315,704; 8,326,418; 8,337,404; 8,340,771; 8,346,354; 8,352,031;8,374,696; 8,374,701; 8,379,952; 8,382,667; 8,401,634; 8,412,334;8,412,338; 8,417,344; 8,423,155; 8,428,726; 8,452,387; 8,454,555;8,457,747; 8,467,878; 8,478,428; 8,485,979; 8,489,185; 8,498,699;8,515,538; 8,536,667; 8,538,523; 8,538,543; 8,548,583; 8,548,594;8,548,604; 8,560,073; 8,562,536; 8,562,660; 8,565,867; 8,571,643;8,571,653; 8,588,933; 8,591,419; 8,600,521; 8,603,790; 8,606,360;8,615,309; 8,630,705; 8,634,922; 8,641,646; 8,644,954; 8,649,871;8,652,187; 8,660,666; 8,666,501; 8,676,324; 8,676,330; 8,684,921;8,694,118; 8,700,163; 8,712,547; 8,716,447; 8,718,779; 8,725,243;8,738,126; 8,744,562; 8,761,868; 8,762,065; 8,768,471; 8,781,597;8,815,582; 8,827,912; 8,831,732; 8,843,210; 8,849,409; 8,852,100;8,855,775; 8,858,440; 8,864,806; 8,868,172; 8,868,177; 8,874,205;8,874,218; 8,874,227; 8,888,702; 8,914,122; 8,918,178; 8,934,967;8,942,817; 8,945,006; 8,948,855; 8,965,514; 8,968,376; 8,972,004;8,972,013; 8,983,155; 8,983,628; 8,983,629; 8,985,119; 8,989,863;8,989,867; 9,014,804; 9,014,823; 9,020,582; 9,020,598; 9,020,789;9,026,218; 9,031,655; 9,042,201; 9,042,988; 9,043,001; 9,044,188;9,050,469; 9,056,195; 9,067,054; 9,067,070; 9,079,940; 9,089,707;9,089,719; 9,095,303; 9,095,314; 9,108,041; 9,113,801; 9,119,533;9,135,400; 9,138,580; 9,162,051; 9,162,052; 9,174,045; 9,174,066;9,186,060; 9,186,106; 9,204,838; 9,204,998; 9,220,910; 9,233,246;9,233,258; 9,235,685; 9,238,150; 9,241,647; 9,242,067; 9,242,092;9,248,286; 9,249,200; 9,249,234; 9,254,383; 9,259,591; 9,265,660;9,265,661; 9,265,662; 9,265,663; 9,265,931; 9,265,946; 9,272,145;9,283,394; 9,284,353; 9,289,599; 9,302,109; 9,309,296; 9,314,633;9,314,635; 9,320,900; 9,326,720; 9,332,939; 9,333,347; 9,339,654;9,345,886; 9,358,381; 9,359,449; 9,364,674; 9,365,628; 9,375,571;9,375,573; 9,381,346; 9,394,347; 9,399,133; 9,399,134; 9,402,994;9,403,000; 9,403,001; 9,403,038; 9,409,022; 9,409,028; 9,415,219;9,415,222; 9,427,581; 9,440,063; 9,458,208; 9,468,761; 9,474,852;9,480,845; 9,492,656; 9,492,678; 9,501,829; 9,504,390; 9,505,817;9,522,085; 9,522,282; 9,526,902; 9,533,147; 9,533,151; 9,538,951;9,545,226; 9,545,510; 9,561,380; 9,566,426; 9,579,506; 9,586,047;9,592,003; 9,592,004; 9,592,409; 9,604,067; 9,604,073; 9,610,442;9,622,675; 9,623,240; 9,643,017; 9,643,019; 9,656,075; 9,662,069;9,662,490; 9,675,794; 9,675,809; 9,682,232; 9,682,241; 9,700,256;9,700,716; 9,700,723; 9,707,390; 9,707,391; 9,717,904; 9,729,252;9,737,230; 20010003799; 20010029391; 20020013612; 20020072776;20020072782; 20020099417; 20020099418; 20020151939; 20030023282;20030045914; 20030083716; 20030114886; 20030181954; 20030195574;20030236557; 20030236558; 20040015204; 20040015205; 20040073273;20040138721; 20040153129; 20040172089; 20040172091; 20040172094;20040193220; 20040243182; 20040260356; 20050027284; 20050033379;20050043774; 20050049651; 20050137645; 20050149123; 20050149157;20050154419; 20050154426; 20050165458; 20050182288; 20050182450;20050182453; 20050182467; 20050182468; 20050182469; 20050187600;20050192644; 20050192647; 20050197590; 20050197675; 20050197678;20050209654; 20050209664; 20050209665; 20050209666; 20050216070;20050216071; 20050251220; 20050267542; 20060009815; 20060047325;20060052657; 20060064138; 20060064139; 20060064140; 20060079936;20060111644; 20060129202; 20060142802; 20060155348; 20060167497;20060173493; 20060173494; 20060173495; 20060195154; 20060206155;20060212090; 20060212091; 20060217781; 20060224216; 20060259077;20060282123; 20060293721; 20060293723; 20070005115; 20070021800;20070043401; 20070060954; 20070060984; 20070066997; 20070067003;20070067004; 20070093870; 20070100377; 20070100378; 20070100392;20070112404; 20070150024; 20070150025; 20070162085; 20070173902;20070198063; 20070213786; 20070233192; 20070233193; 20070255320;20070255379; 20080021341; 20080027347; 20080027348; 20080027515;20080033502; 20080039904; 20080065183; 20080077191; 20080086182;20080091240; 20080125829; 20080140141; 20080147137; 20080154332;20080161894; 20080167571; 20080183097; 20080269542; 20080269833;20080269834; 20080269840; 20090018462; 20090036950; 20090054946;20090088680; 20090093403; 20090118780; 20090163982; 20090171405;20090187230; 20090234419; 20090276011; 20090276012; 20090280153;20090326605; 20100003656; 20100004705; 20100004717; 20100057159;20100063563; 20100106217; 20100114190; 20100114192; 20100114193;20100125219; 20100125304; 20100145428; 20100191304; 20100198098;20100198296; 20100204749; 20100268288; 20100274303; 20100274308;20100292602; 20110009920; 20110021899; 20110028799; 20110029038;20110029044; 20110034912; 20110054569; 20110077721; 20110092800;20110098778; 20110105998; 20110125203; 20110130615; 20110137381;20110152967; 20110152988; 20110160795; 20110166430; 20110166546;20110172554; 20110172725; 20110172732; 20110172739; 20110178441;20110178442; 20110190569; 20110201944; 20110213222; 20110224602;20110224749; 20110230701; 20110230938; 20110257517; 20110264182;20110270095; 20110270096; 20110270346; 20110270347; 20110276107;20110276112; 20110282225; 20110295344; 20110295345; 20110295346;20110295347; 20110301529; 20110307030; 20110311489; 20110319975;20120016336; 20120016432; 20120029591; 20120029601; 20120046711;20120059431; 20120078323; 20120083700; 20120083701; 20120101326;20120116741; 20120158092; 20120179228; 20120184801; 20120185020;20120191158; 20120203079; 20120209346; 20120226130; 20120232327;20120265262; 20120303080; 20120310050; 20120316622; 20120330369;20130006332; 20130018438; 20130018439; 20130018440; 20130019325;20130046358; 20130066350; 20130066392; 20130066395; 20130072996;20130089503; 20130090454; 20130096441; 20130131753; 20130165846;20130178913; 20130184639; 20130184792; 20130204144; 20130225953;20130225992; 20130231721; 20130238049; 20130238050; 20130238053;20130244323; 20130245464; 20130245486; 20130245711; 20130245712;20130253612; 20130261703; 20130274625; 20130281890; 20130289653;20130289669; 20130296406; 20130296637; 20130304159; 20130309278;20130310909; 20130317580; 20130338450; 20140039290; 20140039336;20140039578; 20140046203; 20140046407; 20140052213; 20140056815;20140058189; 20140058292; 20140074188; 20140081071; 20140081353;20140094720; 20140100633; 20140107397; 20140107398; 20140113367;20140128938; 20140135680; 20140135886; 20140142653; 20140142654;20140142669; 20140155772; 20140155952; 20140163643; 20140213842;20140213961; 20140214135; 20140235826; 20140236272; 20140243613;20140243714; 20140257118; 20140257132; 20140257430; 20140257437;20140257438; 20140275716; 20140276194; 20140277255; 20140277256;20140288620; 20140303452; 20140324118; 20140330334; 20140330335;20140330336; 20140336514; 20140336730; 20140343463; 20140357936;20140358067; 20140358193; 20140378851; 20150005592; 20150005839;20150012054; 20150018893; 20150025422; 20150032044; 20150032178;20150051655; 20150051656; 20150051657; 20150051658; 20150051659;20150057715; 20150072394; 20150073237; 20150073505; 20150119689;20150119794; 20150119956; 20150142082; 20150148878; 20150157859;20150165226; 20150174398; 20150174405; 20150174407; 20150182753;20150182756; 20150190636; 20150190637; 20150196246; 20150202428;20150208978; 20150216469; 20150231330; 20150238761; 20150265830;20150265836; 20150283265; 20150297719; 20150297889; 20150306392;20150343222; 20150352362; 20150360030; 20150366482; 20150374973;20150374993; 20160001096; 20160008620; 20160012749; 20160030666;20160045162; 20160045731; 20160051818; 20160058359; 20160074660;20160081610; 20160114165; 20160121114; 20160121116; 20160135727;20160136423; 20160144175; 20160151628; 20160158554; 20160175607;20160199656; 20160199662; 20160206236; 20160222073; 20160232811;20160243381; 20160249846; 20160250465; 20160263376; 20160279021;20160279022; 20160279023; 20160279024; 20160279025; 20160279267;20160279410; 20160279435; 20160287869; 20160287895; 20160303396;20160303402; 20160310070; 20160331952; 20160331974; 20160331982;20160339237; 20160339238; 20160339239; 20160339242; 20160346542;20160361540; 20160361546; 20160367808; 20160375245; 20170007820;20170027812; 20170043160; 20170056467; 20170056642; 20170066806;20170079573; 20170080050; 20170087364; 20170095199; 20170095670;20170113042; 20170113057; 20170120043; 20170120052; 20170143550;20170143963; 20170143986; 20170150916; 20170150921; 20170151433;20170157402; 20170164894; 20170189707; 20170198017; and 20170224994.

Brain-To-Brain

Interface A brain-brain interface is a direct communication pathwaybetween the brain of one animal and the brain of another animal. Brainto brain interfaces have been used to help rats collaborate with eachother. When a second rat was unable to choose the correct lever, thefirst rat noticed (not getting a second reward), and produced a round oftask-related neuron firing that made the second rat more likely tochoose the correct lever. Human studies have also been conducted.

In 2013, researcher from the University of Washington were able to useelectrical brain recordings and a form of magnetic stimulation to send abrain signal to a recipient, which caused the recipient to hit the firebutton on a computer game. In 2015, researchers linked up multiplebrains, of both monkeys and rats, to form an “organic computer.” It ishypothesized that by using brain-to-brain interfaces (BTBIs) abiological computer, or brain-net, could be constructed using animalbrains as its computational units. Initial exploratory work demonstratedcollaboration between rats in distant cages linked by signals fromcortical microelectrode arrays implanted in their brains. The rats wererewarded when actions were performed by the “decoding rat” whichconformed to incoming signals and when signals were transmitted by the“encoding rat” which resulted in the desired action. In the initialexperiment the rewarded action was pushing a lever in the remotelocation corresponding to the position of a lever near a lighted LED atthe home location. About a month was required for the rats to acclimatethemselves to incoming “brainwaves.” When a decoding rat was unable tochoose the correct lever, the encoding rat noticed (not getting anexpected reward), and produced a round of task-related neuron firingthat made the second rat more likely to choose the correct lever.

In another study, electrical brain readings were used to trigger a formof magnetic stimulation, to send a brain signal based on brain activityon a subject to a recipient, which caused the recipient to hit the firebutton on a computer game.

Brain-To-Computer Interface

A brain-computer interface (BCI), sometimes called a neural-controlinterface (NCI), mind-machine interface (MMI), direct neural interface(DNI), or brain-machine interface (BMI), is a direct communicationpathway between an enhanced or wired brain and an external device. BCIdiffers from neuromodulation in that it allows for bidirectionalinformation flow. BCIs are often directed at researching, mapping,assisting, augmenting, or repairing human cognitive or sensory-motorfunctions.

Synthetic telepathy, also known as techlepathy or psychotronics(geeldon.wordpress.com/2010/09/06/synthetic-telepathy-also-known-as-techlepathy-or-psychotronics),describes the process of use of brain-computer interfaces by which humanthought (as electromagnetic radiation) is intercepted, processed bycomputer and a return signal generated that is perceptible by the humanbrain. Dewan, E. M., “Occipital Alpha Rhythm Eye Position and LensAccommodation.” Nature 214, 975-977 (3 Jun. 1967), demonstrates themental control of Alpha waves, turning them on and off, to produce Morsecode representations of words and phrases by thought alone. U.S. Pat.No. 3,951,134 proposes remotely monitoring and altering brainwaves usingradio, and references demodulating the waveform, displaying it to anoperator for viewing and passing this to a computer for furtheranalysis. In 1988, Farwell, L. A, & Donchin, E. (1988). Talking off thetop of your head: toward a mental prosthesis utilizing event-relatedbrain potentials. Electroencephalography and Clinical Neurophysiology,70(6), 510-523 describes a method of transmitting linguistic informationusing the P300 response system, which combines matching observedinformation to what the subject was thinking of. In this case, beingable to select a letter of the alphabet that the subject was thinkingof. In theory, any input could be used and a lexicon constructed. U.S.Pat. No. 6,011,991 describes a method of monitoring an individual'sbrainwaves remotely, for the purposes of communication, and outlines asystem that monitors an individual's brainwaves via a sensor, thentransmits this information, specifically by satellite, to a computer foranalysis. This analysis would determine if the individual was attemptingto communicate a “word, phrase, or thought corresponding to the matchedstored normalized signal.”

Approaches to synthetic telepathy can be categorized into two majorgroups, passive and active. Like sonar, the receiver can take part orpassively listen. Passive reception is the ability to “read” a signalwithout first broadcasting a signal. This can be roughly equated totuning into a radio station—the brain generates electromagneticradiation which can be received at a distance. That distance isdetermined by the sensitivity of the receiver, the filters used and thebandwidth required. Most universities would have limited budgets, andreceivers, such as EEG (and similar devices), would be used. A relatedmilitary technology is the surveillance system TEMPEST. Robert G.Malech's approach requires a modulated signal to be broadcast at thetarget. The method uses an active signal, which is interfered with bythe brain's modulation. Thus, the return signal can be used to infer theoriginal brainwave.

Computer mediation falls into two basic categories, interpretative andinteractive. Interpretative mediation is the passive analysis of signalscoming from the human brain. A computer “reads” the signal then comparesthat signal against a database of signals and their meanings. Usingstatistical analysis and repetition, false-positives are reducedovertime. Interactive mediation can be in a passive-active mode oractive-active mode. In this case, passive and active denote the methodof reading and writing to the brain and whether or not they make use ofa broadcast signal. Interactive mediation can also be performed manuallyor via artificial intelligence. Manual interactive mediation involves ahuman operator producing return signals such as speech or images. AImediation leverages the cognitive system of the subject to identifyimages, pre-speech, objects, sounds and other artifacts, rather thandeveloping AI routines to perform such activities. AI based systems mayincorporate natural language processing interfaces that producesensations, mental impressions, humor and conversation to provide amental picture of a computerized personality. Statistical analysis andML techniques, such as neural networks can be used.

ITV News Service (3/1991), reported ultrasound piggybacked on acommercial radio broadcast (100 MHz) aimed at entraining the brains ofIraqi troops and creating feelings of despair. U.S. Pat. No. 5,159,703that refers to a “silent communications system in which nonauralcarriers, in the very low or very high audio frequency range or in theadjacent ultrasonic frequency spectrum, are amplitude or frequencymodulated with the desired intelligence and propagated acoustically orvibrationally, for inducement into the brain, typically through the useof loudspeakers, earphones or piezoelectric transducers.” See:

-   Dr Nick Begich—Controlling the Human Mind, Earth Pulse Press    Anchorage—isbn=1-890693-54-5-   cbcg.org/gjcs1.htm %7C God's Judgment Cometh Soon-   cnslab.ss.uci.edu/muri/research.html, #Dewan, #FarwellDonchin,    #ImaginedSpeechProduction, #Overview, MURI: Synthetic Telepathy-   daprocess.com/01.welcome.html DaProcess of A Federal Investigation-   deepthought.newsvine.com/_news/2012/01/01/9865851-nsa-disinformation-watch-the-watchers-with-me-   deepthought.newsvine.com/_news/2012/01/09/10074589-nsa-disinformation-watch-the-watchers-with-me-part-2-   deepthought.newsvine.com/_news/2012/01/16/10169491-the-nsa-behind-the-curtain-   genamason.wordpress.com/2009/10/18/more-on-synthetic-telepathy/-   io9.com5065304/tips-and-ticks-for-mind-control-from-the-us-military-   newdawnmagazine.com.au/Article/Brain_Zapping_Part_One.html-   pinktentacle.com/2008/12/scientists-extract-images-directly-from-brain/Scientists    extract images directly from brain-   timesofindia.indiames.comHeathSci/US_army_developing_synthetic_telepathy/-   www.bibliotecapleyades.net/ciencia/ciencia_nonlethalWeapons02.htm    Eleanor White—New Devices That ‘Talk’ To The Human Mind Need Debate,    Controls-   www.cbsnews.com/stories/2008/12/31/60 minutes/main4694713.shtml 60    Minutes: Incredible Research Lets Scientists Get A Glimpse At Your    Thoughts-   www.cbsnews.com/video/watch/?id=5119805n&amp;tag=related;photovideo    60 Minutes: Video—Mind Reading-   www.charlesrehn.com/charlesrehn/books/aconversationwithameca/essays/myessays/The    %20NSA.doc-   www.govtrack.us/congress/billtext.xpd?bill=hl 107-2977 Space    Preservation Act of 2001-   www.informaworld.com/smpp/content˜db=all˜content-a785359968 Partial    Amnesia for a Narrative Following Application of Theta Frequency    Electromagnetic Fields-   www.msnbc.msn.com/id/27162401/-   www.psychology.nottingham.ac.uk/staff/lpxdts/TMS %20info.html    Transcranial Magnetic Stimulation-   www.raven1.net/silsoun2.htm Psy-Ops Weaponry Used In The Persian    Gulf War-   www.scdbd.com/doc/24531011/Operation-Mind-Control-   www.scdbd.com/doc/6508206/synthetic-telepathy-and-the-early-mind-wars-   www.slavery.org.uk/Bioeffects    of_Selected_Non-Lethal_Weapons.pdf-Bioeffects of selected non-lethal    weapons-   www.sst.ws/tempstandards.php?pab=1_1 TEMPEST measurement standards-   www.uwe.ac.uk/hlss/research/cpss/Journal_Psycho-Social_Studies/v2-2/SmithC.shtml    Journal of Psycho-Social Studies—Vol 2 (2) 2003—On the Need for New    Criteria of Diagnosis of Psychosis in the Light of Mind Invasive    Technology by Dr. Carole Smith-   www.wired.com/dangerroom/2009/05/pentagon-preps-soldier-telepathy-push-   www.wired.com/wired/archive/7.11/persinger.html This Is Your Brain    on God-   Noah, Shachtman—Pentagon's PCs Bend to Your Brain    www.wired.com/dangerroom/2007/03/the_us_military-   Soldier-Telepathy” Drummond, Katie—Pentagon Preps Soldier Telepathy    Push U.S. Pat. No. 3,951,134-   U.S. Pat. No. 5,159,703 Silent subliminal presentation system-   U.S. Pat. No. 6,011,991-   U.S. Pat. No. 6,587,729 Apparatus for audibly communicating speech    using the radio frequency hearing effect-   Wall, Judy, “Military Use of Mind Control Weapons”, NEXUS, 5/06,    October-November 1998

It is known to analyze EEG patterns to extract an indication of certainvolitional activity (U.S. Pat. No. 6,011,991). This technique describesthat an EEG recording can be matched against a stored normalized signalusing a computer. This matched signal is then translated into thecorresponding reference. The patent application describes a method “asystem capable of identifying particular nodes in an individual's brain,the firings of which affect characteristics such as appetite, hunger,thirst, communication skills” and “devices mounted to the person (e.g.underneath the scalp) may be energized in a predetermined manner orsequence to remotely cause particular identified brain node(s) to befired in order to cause a predetermined feeling or reaction in theindividual” without technical description of implementation. This patentalso describes, that “brain activity (is monitored) by way ofelectroencephalograph (EEG) methods, magnetoencephalograph (MEG)methods, and the like. For example, see U.S. Pat. Nos. 5,816,247 and5,325,862.

See also, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 3,951,134;4,437,064; 4,591,787; 4,613,817; 4,689,559; 4,693,000; 4,700,135;4,733,180; 4,736,751; 4,749,946; 4,753,246; 4,761,611; 4771,239;4,801,882; 4,862,359; 4,913,152; 4,937,525; 4,940,058; 4,947,480;4,949,725; 4,951,674; 4,974,602; 4,982,157; 4,983,912; 4,996,479;5,008,622; 5,012,190; 5,020,538; 5,061,680; 5,092,835; 5,095,270;5,126,315; 5,158,932; 5,159,703; 5,159,928; 5,166,614; 5,187,327;5,198,977; 5,213,338; 5,241,967; 5,243,281; 5,243,517; 5,263,488;5,265,611; 5,269,325; 5,282,474; 5,283,523; 5,291,888; 5,303,705;5,307,807; 5,309,095; 5,311,129; 5,323,777; 5,325,862; 5,326,745;5,339,811; 5,417,211; 5,418,512; 5,442,289; 5,447,154; 5,458,142;5,469,057; 5,476,438; 5,496,798; 5,513,649; 5,515,301; 5,552,375;5,579,241; 5,594,849; 5,600,243; 5,601,081; 5,617,856; 5,626,145;5,656,937; 5,671,740; 5,682,889; 5,701,909; 5,706,402; 5,706,811;5,729,046; 5,743,854; 5,743,860; 5,752,514; 5,752,911; 5,755,227;5,761,332; 5,762,611; 5,767,043; 5,771,261; 5,771,893; 5,771,894;5,797,853; 5,813,993; 5,815,413; 5,842,986; 5,857,978; 5,885,976;5,921,245; 5,938,598; 5,938,688; 5,970,499; 6,002,254; 6,011,991;6,023,161; 6,066,084; 6,069,369; 6,080,164; 6,099,319; 6,144,872;6,154,026; 6,155,966; 6,167,298; 6,167,311; 6,195,576; 6,230,037;6,239,145; 6,263,189; 6,290,638; 6,354,087; 6,356,079; 6,370,414;6,374,131; 6,385,479; 6,418,344; 6,442,948; 6,470,220; 6,488,617;6,516,246; 6,526,415; 6,529,759; 6,538,436; 6,539,245; 6,539,263;6,544,170; 6,547,746; 6,557,558; 6,587,729; 6,591,132; 6,609,030;6,611,698; 6,648,822; 6,658,287; 6,665,552; 6,665,553; 6,665,562;6,684,098; 6,687,525; 6,695,761; 6,697,660; 6,708,051; 6,708,064;6,708,184; 6,725,080; 6,735,460; 6,774,929; 6,785,409; 6,795,724;6,804,661; 6,815,949; 6,853,186; 6,856,830; 6,873,872; 6,876,196;6,885,192; 6,907,280; 6,926,921; 6,947,790; 6,978,179; 6,980,863;6,983,184; 6,983,264; 6,996,261; 7,022,083; 7,023,206; 7,024,247;7,035,686; 7,038,450; 7,039,266; 7,039,547; 7,053,610; 7,062,391;7,092,748; 7,105,824; 7,116,102; 7,120,486; 7,130,675; 7,145,333;7,171,339; 7,176,680; 7,177,675; 7,183,381; 7,186,209; 7,187,169;7,190,826; 7,193,413; 7,196,514; 7,197,352; 7,199,708; 7,209,787;7,218,104; 7,222,964; 7,224,282; 7,228,178; 7,231,254; 7,242,984;7,254,500; 7,258,659; 7,269,516; 7,277,758; 7,280,861; 7,286,871;7,313,442; 7,324,851; 7,334,892; 7,338,171; 7,340,125; 7,340,289;7,346,395; 7,353,064; 7,353,065; 7,369,896; 7,371,365; 7,376,459;7,394,246; 7,400,984; 7,403,809; 7,403,820; 7,409,321; 7,418,290;7,420,033; 7,437,196; 7,440,789; 7,453,263; 7,454,387; 7,457,653;7,461,045; 7,462,155; 7,463,024; 7,466,132; 7,468,350; 7,482,298;7,489,964; 7,502,720; 7,539,528; 7,539,543; 7,553,810; 7,565,200;7,565,809; 7,567,693; 7,570,054; 7,573,264; 7,573,268; 7,580,798;7,603,174; 7,608,579; 7,613,502; 7,613,519; 7,613,520; 7,620,456;7,623,927; 7,623,928; 7,625,340; 7,627,370; 7,647,098; 7,649,351;7,653,433; 7,672,707; 7,676,263; 7,678,767; 7,697,979; 7,706,871;7,715,894; 7,720,519; 7,729,740; 7,729,773; 7,733,973; 7,734,340;7,737,687; 7,742,820; 7,746,979; 7,747,325; 7,747,326; 7,747,551;7,756,564; 7,763,588; 7,769,424; 7,771,341; 7,792,575; 7,800,493;7,801,591; 7,801,686; 7,831,305; 7,834,627; 7,835,787; 7,840,039;7,840,248; 7,840,250; 7,853,329; 7,856,264; 7,860,552; 7,873,411;7,881,760; 7,881,770; 7,882,135; 7,891,814; 7,892,764; 7,894,903;7,895,033; 7,904,139; 7,904,507; 7,908,009; 7,912,530; 7,917,221;7,917,225; 7,929,693; 7,930,035; 7,932,225; 7,933,727; 7,937,152;7,945,304; 7,962,204; 7,974,787; 7,986,991; 7,988,969; 8,000,767;8,000,794; 8,001,179; 8,005,894; 8,010,178; 8,014,870; 8,027,730;8,029,553; 8,032,209; 8,036,736; 8,055,591; 8,059,879; 8,065,360;8,069,125; 8,073,631; 8,082,215; 8,083,786; 8,086,563; 8,116,874;8,116,877; 8,121,694; 8,121,695; 8,150,523; 8,150,796; 8,155,726;8,160,273; 8,185,382; 8,190,248; 8,190,264; 8,195,593; 8,209,224;8,212,556; 8,222,378; 8,224,433; 8,229,540; 8,239,029; 8,244,552;8,244,553; 8,248,069; 8,249,316; 8,270,814; 8,280,514; 8,285,351;8,290,596; 8,295,934; 8,301,222; 8,301,257; 8,303,636; 8,304,246;8,305,078; 8,308,646; 8,315,703; 8,334,690; 8,335,715; 8,335,716;8,337,404; 8,343,066; 8,346,331; 8,350,804; 8,354,438; 8,356,004;8,364,271; 8,374,412; 8,374,696; 8,380,314; 8,380,316; 8,380,658;8,386,312; 8,386,313; 8,388,530; 8,392,250; 8,392,251; 8,392,253;8,392,254; 8,392,255; 8,396,545; 8,396,546; 8,396,744; 8,401,655;8,406,838; 8,406,848; 8,412,337; 8,423,144; 8,423,297; 8,429,225;8,431,537; 8,433,388; 8,433,414; 8,433,418; 8,439,845; 8,444,571;8,445,021; 8,447,407; 8,456,164; 8,457,730; 8,463,374; 8,463,378;8,463,386; 8,463,387; 8,464,288; 8,467,878; 8,473,345; 8,483,795;8,484,081; 8,487,760; 8,492,336; 8,494,610; 8,494,857; 8,494,905;8,498,697; 8,509,904; 8,519,705; 8,527,029; 8,527,035; 8,529,463;8,532,756; 8,532,757; 8,533,042; 8,538,513; 8,538,536; 8,543,199;8,548,786; 8,548,852; 8,553,956; 8,554,325; 8,559,645; 8,562,540;8,562,548; 8,565,606; 8,568,231; 8,571,629; 8,574,279; 8,586,019;8,587,304; 8,588,933; 8,591,419; 8,593,141; 8,600,493; 8,600,696;8,603,790; 8,606,592; 8,612,005; 8,613,695; 8,613,905; 8,614,254;8,614,873; 8,615,293; 8,615,479; 8,615,664; 8,618,799; 8,626,264;8,628,328; 8,635,105; 8,648,017; 8,652,189; 8,655,428; 8,655,437;8,655,817; 8,658,149; 8,660,649; 8,666,099; 8,679,009; 8,682,441;8,690,748; 8,693,765; 8,700,167; 8,703,114; 8,706,205; 8,706,206;8,706,241; 8,706,518; 8,712,512; 8,716,447; 8,721,695; 8,725,243;8,725,668; 8,725,669; 8,725,796; 8,731,650; 8,733,290; 8,738,395;8,762,065; 8,762,202; 8,768,427; 8,768,447; 8,781,197; 8,781,597;8,786,624; 8,798,717; 8,814,923; 8,815,582; 8,825,167; 8,838,225;8,838,247; 8,845,545; 8,849,390; 8,849,392; 8,855,775; 8,858,440;8,868,173; 8,874,439; 8,888,702; 8,893,120; 8,903,494; 8,907,668;8,914,119; 8,918,176; 8,922,376; 8,933,696; 8,934,965; 8,938,289;8,948,849; 8,951,189; 8,951,192; 8,954,293; 8,955,010; 8,961,187;8,974,365; 8,977,024; 8,977,110; 8,977,362; 8,993,623; 9,002,458;9,014,811; 9,015,087; 9,020,576; 9,026,194; 9,026,218; 9,026,372;9,031,658; 9,034,055; 9,034,923; 9,037,224; 9,042,074; 9,042,201;9,042,988; 9,044,188; 9,053,516; 9,063,183; 9,064,036; 9,069,031;9,072,482; 9,074,976; 9,079,940; 9,081,890; 9,095,266; 9,095,303;9,095,618; 9,101,263; 9,101,276; 9,102,717; 9,113,801; 9,113,803;9,116,201; 9,125,581; 9,125,788; 9,138,156; 9,142,185; 9,155,373;9,161,715; 9,167,979; 9,173,609; 9,179,854; 9,179,875; 9,183,351;9,192,300; 9,198,621; 9,198,707; 9,204,835; 9,211,076; 9,211,077;9,213,074; 9,229,080; 9,230,539; 9,233,244; 9,238,150; 9,241,665;9,242,067; 9,247,890; 9,247,911; 9,248,003; 9,248,288; 9,249,200;9,249,234; 9,251,566; 9,254,097; 9,254,383; 9,259,482; 9,259,591;9,261,573; 9,265,943; 9,265,965; 9,271,679; 9,280,784; 9,283,279;9,284,353; 9,285,249; 9,289,595; 9,302,069; 9,309,296; 9,320,900;9,329,758; 9,331,841; 9,332,939; 9,333,334; 9,336,535; 9,336,611;9,339,227; 9,345,609; 9,351,651; 9,357,240; 9,357,298; 9,357,970;9,358,393; 9,359,449; 9,364,462; 9,365,628; 9,367,738; 9,368,018;9,370,309; 9,370,667; 9,375,573; 9,377,348; 9,377,515; 9,381,352;9,383,208; 9,392,955; 9,394,347; 9,395,425; 9,396,669; 9,401,033;9,402,558; 9,403,038; 9,405,366; 9,410,885; 9,411,033; 9,412,233;9,415,222; 9,418,368; 9,421,373; 9,427,474; 9,438,650; 9,440,070;9,445,730; 9,446,238; 9,448,289; 9,451,734; 9,451,899; 9,458,208;9,460,400; 9,462,733; 9,463,327; 9,468,541; 9,471,978; 9,474,852;9,480,845; 9,480,854; 9,483,117; 9,486,381; 9,486,389; 9,486,618;9,486,632; 9,492,114; 9,495,684; 9,497,017; 9,498,134; 9,498,634;9,500,722; 9,505,817; 9,517,031; 9,517,222; 9,519,981; 9,521,958;9,534,044; 9,538,635; 9,539,118; 9,556,487; 9,558,558; 9,560,458;9,560,967; 9,560,984; 9,560,986; 9,563,950; 9,568,564; 9,572,996;9,579,035; 9,579,048; 9,582,925; 9,584,928; 9,588,203; 9,588,490;9,592,384; 9,600,138; 9,604,073; 9,612,295; 9,618,591; 9,622,660;9,622,675; 9,630,008; 9,642,553; 9,642,554; 9,643,019; 9,646,248;9,649,501; 9,655,573; 9,659,186; 9,664,856; 9,665,824; 9,665,987;9,675,292; 9,681,814; 9,682,232; 9,684,051; 9,685,600; 9,687,562;9,694,178; 9,694,197; 9,713,428; 9,713,433; 9,713,444; 9,713,712;D627476; RE44097; RE46209; 20010009975; 20020103428; 20020103429;20020158631; 20020173714; 20030004429; 20030013981; 20030018277;20030081818; 20030093004; 20030097159; 20030105408; 20030158495;20030199749; 20040019370; 20040034299; 20040092809; 20040127803;20040186542; 20040193037; 20040210127; 20040210156; 20040263162;20050015205; 20050033154; 20050043774; 20050059874; 20050216071;20050256378; 20050283053; 20060074822; 20060078183; 20060100526;20060135880; 20060225437; 20070005391; 20070036355; 20070038067;20070043392; 20070049844; 20070083128; 20070100251; 20070165915;20070167723; 20070191704; 20070197930; 20070239059; 20080001600;20080021340; 20080091118; 20080167571; 20080249430; 20080304731;20090018432; 20090082688; 20090099783; 20090149736; 20090179642;20090216288; 20090299169; 20090312624; 20090318794; 20090319001;20090319004; 20100010366; 20100030097; 20100049482; 20100056276;20100069739; 20100092934; 20100094155; 20100113959; 20100131034;20100174533; 20100197610; 20100219820; 20110015515; 20110015539;20110046491; 20110082360; 20110110868; 20110150253; 20110182501;20110217240; 20110218453; 20110270074; 20110301448; 20120021394;20120143104; 20120150262; 20120191542; 20120232376; 20120249274;20120253168; 20120271148; 20130012804; 20130013667; 20130066394;20130072780; 20130096453; 20130150702; 20130165766; 20130211238;20130245424; 20130251641; 20130255586; 20130304472; 20140005518;20140058241; 20140062472; 20140077612; 20140101084; 20140121565;20140135873; 20140142448; 20140155730; 20140159862; 20140206981;20140243647; 20140243652; 20140245191; 20140249445; 20140249447;20140271483; 20140275891; 20140276013; 20140276014; 20140276187;20140276702; 20140277582; 20140279746; 20140296733; 20140297397;20140300532; 20140303424; 20140303425; 20140303511; 20140316248;20140323899; 20140328487; 20140330093; 20140330394; 20140330580;20140335489; 20140336489; 20140336547; 20140343397; 20140343882;20140348183; 20140350380; 20140354278; 20140357507; 20140357932;20140357935; 20140358067; 20140364721; 20140370479; 20140371573;20140371611; 20140378815; 20140378830; 20150005840; 20150005841;20150008916; 20150011877; 20150017115; 20150018665; 20150018702;20150018705; 20150018706; 20150019266; 20150025422; 20150025917;20150026446; 20150030220; 20150033363; 20150044138; 20150065838;20150065845; 20150069846; 20150072394; 20150073237; 20150073249;20150080695; 20150080703; 20150080753; 20150080985; 20150088024;20150088224; 20150091730; 20150091791; 20150096564; 20150099962;20150105844; 20150112403; 20150119658; 20150119689; 20150119698;20150119745; 20150123653; 20150133811; 20150133812; 20150133830;20150140528; 20150141529; 20150141773; 20150148619; 20150150473;20150150475; 20150151142; 20150154721; 20150154764; 20150157271;20150161738; 20150174403; 20150174418; 20150178631; 20150178978;20150182417; 20150186923; 20150192532; 20150196800; 20150201879;20150202330; 20150206051; 20150206174; 20150212168; 20150213012;20150213019; 20150213020; 20150215412; 20150216762; 20150219729;20150219732; 20150220830; 20150223721; 20150226813; 20150227702;20150230719; 20150230744; 20150231330; 20150231395; 20150231405;20150238104; 20150248615; 20150253391; 20150257700; 20150264492;20150272461; 20150272465; 20150283393; 20150289813; 20150289929;20150293004; 20150294074; 20150297108; 20150297139; 20150297444;20150297719; 20150304048; 20150305799; 20150305800; 20150305801;20150306057; 20150306390; 20150309582; 20150313496; 20150313971;20150315554; 20150317447; 20150320591; 20150324544; 20150324692;20150327813; 20150328330; 20150335281; 20150335294; 20150335876;20150335877; 20150343242; 20150359431; 20150360039; 20150366503;20150370325; 20150374250; 20160000383; 20160005235; 20160008489;20160008598; 20160008620; 20160008632; 20160012011; 20160012583;20160015673; 20160019434; 20160019693; 20160022165; 20160022168;20160022207; 20160022981; 20160023016; 20160029958; 20160029959;20160029998; 20160030666; 20160030834; 20160038049; 20160038559;20160038770; 20160048659; 20160048948; 20160048965; 20160051161;20160051162; 20160055236; 20160058322; 20160063207; 20160063883;20160066838; 20160070436; 20160073916; 20160073947; 20160081577;20160081793; 20160082180; 20160082319; 20160084925; 20160086622;20160095838; 20160097824; 20160100769; 20160103487; 20160103963;20160109851; 20160113587; 20160116472; 20160116553; 20160120432;20160120436; 20160120480; 20160121074; 20160128589; 20160128632;20160129249; 20160131723; 20160135748; 20160139215; 20160140975;20160143540; 20160143541; 20160148077; 20160148400; 20160151628;20160157742; 20160157777; 20160157828; 20160158553; 20160162652;20160164813; 20160166207; 20160166219; 20160168137; 20160170996;20160170998; 20160171514; 20160174862; 20160174867; 20160175557;20160175607; 20160184599; 20160198968; 20160203726; 20160204937;20160205450; 20160206581; 20160206871; 20160206877; 20160210872;20160213276; 20160219345; 20160220163; 20160220821; 20160222073;20160223622; 20160223627; 20160224803; 20160235324; 20160238673;20160239966; 20160239968; 20160240212; 20160240765; 20160242665;20160242670; 20160250473; 20160256130; 20160257957; 20160262680;20160275536; 20160278653; 20160278662; 20160278687; 20160278736;20160279267; 20160287117; 20160287308; 20160287334; 20160287895;20160299568; 20160300252; 20160300352; 20160302711; 20160302720;20160303396; 20160303402; 20160306844; 20160313408; 20160313417;20160313418; 20160321742; 20160324677; 20160324942; 20160334475;20160338608; 20160339300; 20160346530; 20160357003; 20160360970;20160361532; 20160361534; 20160371387; 20170000422; 20170014080;20170020454; 20170021158; 20170021161; 20170027517; 20170032527;20170039591; 20170039706; 20170041699; 20170042474; 20170042476;20170042827; 20170043166; 20170043167; 20170045601; 20170052170;20170053082; 20170053088; 20170053461; 20170053665; 20170056363;20170056467; 20170056655; 20170065199; 20170065349; 20170065379;20170065816; 20170066806; 20170079538; 20170079543; 20170080050;20170080256; 20170085547; 20170085855; 20170086729; 20170087367;20170091418; 20170095174; 20170100051; 20170105647; 20170107575;20170108926; 20170119270; 20170119271; 20170120043; 20170131293;20170133576; 20170133577; 20170135640; 20170140124; 20170143986;20170146615; 20170146801; 20170147578; 20170148213; 20170148592;20170150925; 20170151435; 20170151436; 20170154167; 20170156674;20170165481; 20170168121; 20170168568; 20170172446; 20170173391;20170178001; 20170178340; 20170180558; 20170181252; 20170182176;20170188932; 20170189691; 20170190765; 20170196519; 20170197081;20170198017; 20170199251; 20170202476; 20170202518; 20170206654;20170209044; 20170209062; 20170209225; 20170209389; and 20170212188.

Brain Entrainment

Brain entrainment, also referred to as brainwave synchronization andneural entrainment, refers to the capacity of the brain to naturallysynchronize its brainwave frequencies with the rhythm of periodicexternal stimuli, most commonly auditory, visual, or tactile. Brainwaveentrainment technologies are used to induce various brain states, suchas relaxation or sleep, by creating stimuli that occur at regular,periodic intervals to mimic electrical cycles of the brain during thedesired states, thereby “training” the brain to consciously alterstates. Recurrent acoustic frequencies, flickering lights, or tactilevibrations are the most common examples of stimuli applied to generatedifferent sensory responses. It is hypothesized that listening to thesebeats of certain frequencies one can induce a desired state ofconsciousness that corresponds with specific neural activity. Patternsof neural firing, measured in Hz, correspond with alertness states suchas focused attention, deep sleep, etc.

The term “entrainment” has been used to describe a shared tendency ofmany physical and biological systems to synchronize their periodicityand rhythm through interaction. This tendency has been identified asspecifically pertinent to the study of sound and music generally, andacoustic rhythms specifically. The most ubiquitous and familiar examplesof neuromotor entrainment to acoustic stimuli is observable inspontaneous foot or finger tapping to the rhythmic beat of a song.Exogenous rhythmic entrainment, which occurs outside the body, has beenidentified and documented for a variety of human activities, whichinclude the way people adjust the rhythm of their speech patterns tothose of the subject with whom they communicate, and the rhythmic unisonof an audience clapping. Even among groups of strangers, the rate ofbreathing, locomotive and subtle expressive motor movements, andrhythmic speech patterns have been observed to synchronize and entrain,in response to an auditory stimulus, such as a piece of music with aconsistent rhythm. Furthermore, motor synchronization to repetitivetactile stimuli occurs in animals, including cats and monkeys as well ashumans, with accompanying shifts in electroencephalogram (EEG) readings.Examples of endogenous entrainment, which occurs within the body,include the synchronizing of human circadian sleep-wake cycles to the24-hour cycle of light and dark, and the frequency following response ofhumans to sounds and music.

Neural Oscillations

Neural oscillations are rhythmic or repetitive electrochemical activityin the brain and central nervous system. Such oscillations can becharacterized by their frequency, amplitude and phase. Neural tissue cangenerate oscillatory activity driven by mechanisms within individualneurons, as well as by interactions between them. They may also adjustfrequency to synchronize with the periodic vibration of externalacoustic or visual stimuli. The functional role of neural oscillationsis still not fully understood; however, they have been shown tocorrelate with emotional responses, motor control, and a number ofcognitive functions including information transfer, perception, andmemory. Specifically, neural oscillations, in particular theta activity,are extensively linked to memory function, and coupling between thetaand gamma activity is considered to be vital for memory functions,including episodic memory. Electroencephalography (EEG) has been mostwidely used in the study of neural activity generated by large groups ofneurons, known as neural ensembles, including investigations of thechanges that occur in electroencephalographic profiles during cycles ofsleep and wakefulness. EEG signals change dramatically during sleep andshow a transition from faster frequencies to increasingly slowerfrequencies, indicating a relationship between the frequency of neuraloscillations and cognitive states including awareness and consciousness.

Brainwaves, or neural oscillations, share the fundamental constituentswith acoustic and optical waves, including frequency, amplitude andperiodicity. The synchronous electrical activity of cortical neuralensembles can synchronize in response to external acoustic or opticalstimuli and also entrain or synchronize their frequency and phase tothat of a specific stimulus. Brainwave entrainment is a colloquialismfor such ‘neural entrainment’, which is a term used to denote the way inwhich the aggregate frequency of oscillations produced by thesynchronous electrical activity in ensembles of cortical neurons canadjust to synchronize with the periodic vibration of an externalstimuli, such as a sustained acoustic frequency perceived as pitch, aregularly repeating pattern of intermittent sounds, perceived as rhythm,or of a regularly rhythmically intermittent flashing light

Changes in neural oscillations, demonstrable throughelectroencephalogram (EEG) measurements, are precipitated by listeningto music, which can modulate autonomic arousal ergotropically andtrophotropically, increasing and decreasing arousal respectively.Musical auditory stimulation has also been demonstrated to improveimmune function, facilitate relaxation, improve mood, and contribute tothe alleviation of stress.

The Frequency following response (FFR), also referred to as FrequencyFollowing Potential (FFP), is a specific response to hearing sound andmusic, by which neural oscillations adjust their frequency to match therhythm of auditory stimuli. The use of sound with intent to influencecortical brainwave frequency is called auditory driving, by whichfrequency of neural oscillation is ‘driven’ to entrain with that of therhythm of a sound source.

See, en.wikipedia.org/wiki/Brainwave_entrainment;

U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 5,070,399; 5,306,228;5,409,445; 6,656,137; 7,749,155; 7,819,794; 7,988,613; 8,088,057;8,167,784; 8,213,670; 8,267,851; 8,298,078; 8,517,909; 8,517,912;8,579,793; 8,579,795; 8,597,171; 8,636,640; 8,638,950; 8,668,496;8,852,073; 8,932,218; 8,968,176; 9,330,523; 9,357,941; 9,459,597;9,480,812; 9,563,273; 9,609,453; 9,640,167; 9,707,372; 20050153268;20050182287; 20060106434; 20060206174; 20060281543; 20070066403;20080039677; 20080304691; 20100010289; 20100010844; 20100028841;20100056854; 20100076253; 20100130812; 20100222640; 20100286747;20100298624; 20110298706; 20110319482; 20120003615; 20120053394;20120150545; 20130030241; 20130072292; 20130131537; 20130172663;20130184516; 20130203019; 20130234823; 20130338738; 20140088341;20140107401; 20140114242; 20140154647; 20140174277; 20140275741;20140309484; 20140371516; 20150142082; 20150283019; 20150296288;20150313496; 20150313949; 20160008568; 20160019434; 20160055842;20160205489; 20160235980; 20160239084; 20160345901; 20170034638;20170061760; 20170087330; 20170094385; 20170095157; 20170099713;20170135597; and 20170149945.

-   Carter, J., and H. Russell. “A pilot investigation of auditory and    visual entrainment of brain wave activity in learning disabled    boys.” Texas Researcher 4.1 (1993): 65-75;-   Casciaro, Francesco, et al. “Alpha-rhythm stimulation using brain    entrainment enhances heart rate variability in subjects with reduced    HRV.” World J. Neuroscience 3.04 (2013): 213;-   Helfrich, Randolph F., et al. “Entrainment of brain oscillations by    transcranial alternating current stimulation.” Current Biology 24.3    (2014): 333-339;-   Huang, Tina L., and Christine Charyton. “A comprehensive review of    the psychological effects of brainwave entrainment” Alternative    therapies in health and medicine 14.5 (2008): 38;-   Joyce, Michael, and Dave Siever. “Audio-visual entrainment program    as a treatment for behavior disorders in a school setting.” J.    Neurotherapy 4.2 (2000): 9-25;-   Keitel, Christian, Cliodhna Quigley, and Philipp Ruhnau.    “Stimulus-driven brain oscillations in the alpha range: entrainment    of intrinsic rhythms or frequency-following response?” J.    Neuroscience 34.31 (2014): 10137-10140;-   Lakatos, Peter, et al. “Entrainment of neuronal oscillations as a    mechanism of attentional selection.” Science 320.5872 (2008):    110-113;-   Mori, Toshio, and Shoichi Kai. “Noise-induced entrainment and    stochastic resonance in human brainwaves.” Physical review letters    88.21 (2002): 218101;-   Padmanabhan, R., A. J. Hildreth, and D. Laws. “A prospective,    randomised, controlled study examining binaural beat audio and    pre-operative anxiety in patients undergoing general anaesthesia for    day case surgery.” Anaesthesia 60.9 (2005): 874-877;-   Schalles, Matt D., and Jaime A. Pineda. “Musical sequence learning    and EEG correlates of audiomotor processing.” Behavioural neurology    2015 (2015). www.hindawi.com/journals/bn/2015/638202/-   Thaut, Michael H., David A. Peterson, and Gerald C. McIntosh.    “Temporal entrainment of cognitive functions.” Annals of the New    York Academy of Sciences 1060.1 (2005): 243-254.-   Thut, Gregor, Philippe G. Schyns, and Joachim Gross. “Entrainment of    perceptually relevant brain oscillations by non-invasive rhythmic    stimulation of the human brain.” Frontiers in Psychology 2 (2011);-   Trost, Wiebke, et al. “Getting the beat entrainment of brain    activity by musical rhythm and pleasantness.” NeuroImage 103 (2014):    55-64;-   Will, Udo, and Eric Berg. “Brain wave synchronization and    entrainment to periodic acoustic stimuli.” Neuroscience letters    424.1 (2007): 55-60; and-   Zhuang, Tianbao, Hong Zhao, and Zheng Tang. “A study of brainwave    entrainment based on EEG brain dynamics.” Computer and information    science 2.2 (2009): 80.

A baseline correction of event-related time-frequency measure may bemade to take pre-event baseline activity into consideration. In general,a baseline period is defined by the average of the values within a timewindow preceding the time-locking event There are at least four commonmethods for baseline correction in time-frequency analysis. The methodsinclude various baseline value normalizations. See,

-   Spencer K M, Nestor P G, Perlmutter R, et al. Neural synchrony    indexes disordered perception and cognition in schizophrenia. Proc    Natl Acad Sci USA. 2004; 101:17288-17293;-   Hoogenboom N, Schoffelen J M, Oostenveld R, Parkes L M, Fries P.    Localizing human visual gamma-band activity in frequency, time and    space. Neuroimage. 2006; 29:764-773;-   Le Van Quyen M, Foucher J, Lachaux J, et al. Comparison of Hilbert    transform and wavelet methods for the analysis of neuronal    synchrony. J Neurosci Methods. 2001; 111:83-98,-   Lachaux J P, Rodriguez E, Martinerie J, Varela F J. Measuring phase    synchrony in brain signals. Hum Brain Mapp. 1999; 8:194-208,-   Rodriguez E, George N, Lachaux J P, Martinerie J, Renault B, Varela    F J. Perception's shadow: long-distance synchronization of human    brain activity. Nature. 1999; 397:430-433.,-   Canolty R T, Edwards E, Dalal S S, et al. High gamma power is    phase-locked to theta oscillations in human neocortex. Science.    2006; 313:1626-1628.

The question of whether different emotional states are associated withspecific patterns of physiological response has long being a subject ofneuroscience research See, for example:

-   James W (1884.) What is an emotion? Mind 9: 188-205; Lacey J I,    Bateman D E, Vanlehn R (1953) Autonomic response specificity; an    experimental study. Psychosom Med 15: 8-21;-   Levenson R W, Heider K, Ekman P, Friesen W V (1992) Emotion and    Autonomic Nervous-System Activity in the Minangkabau of West    Sumatra. J Pers Soc Psychol 62: 972-988.

Some studies have indicated that the physiological correlates ofemotions are likely to be found in the central nervous system (CNS).See, for example:

-   Buck R (1999) The biological affects: A typology. Psychological    Review 106: 301-336; Izard C E (2007) Basic Emotions, Natural Kinds,    Emotion Schemas, and a New Paradigm. Perspect Psychol Sci 2:    260-280;-   Panksepp J (2007) Neurologizing the Psychology of Affects How    Appraisal-Based Constructivism and Basic Emotion Theory Can Coexist    Perspect Psychol Sci 2: 281-296.

Electroencephalograms (EEG) and functional Magnetic Resonance Imaging,fMRI have been used to study specific brain activity associated withdifferent emotional states. Mauss and Robinson, in their review paper,have indicated that “emotional state is likely to involve circuitsrather than any brain region considered in isolation” (Mauss I B,Robinson M D (2009) Measures of emotion: A review. Cogn Emot 23:209-237.)

The amplitude, latency from the stimulus, and covariance (in the case ofmultiple electrode sites) of each component can be examined inconnection with a cognitive task (ERP) or with no task (EP).Steady-state visually evoked potentials (SSVEPs) use a continuoussinusoidally-modulated flickering light, typically superimposed in frontof a TV monitor displaying a cognitive task. The brain response in anarrow frequency band containing the stimulus frequency is measured.Magnitude, phase, and coherence (in the case of multiple electrodesites) may be related to different parts of the cognitive task Brainentrainment may be detected through EEG or MEG activity.

Brain entrainment may be detected through EEG or MEG activity. See:

-   Abeln, Vera, et al. “Brainwave entrainment for better sleep and    post-sleep state of young elite soccer players-A pilot study.”    European J. Sport science 14.5 (2014): 393-402;-   Acton, George. “Methods for independent entrainment of visual field    zones.” U.S. Pat. No. 9,629,976. 25 Apr. 2017;-   Albouy, Philippe, et al. “Selective entrainment of theta    oscillations in the dorsal stream causally enhances auditory working    memory performance.” Neuron 94.1 (2017): 193-206.-   Amengual, J., et al. “P018 Local entrainment and distribution across    cerebral networks of natural oscillations elicited in implanted    epilepsy patients by intracranial stimulation: Paving the way to    develop causal connectomics of the healthy human brain.” Clin.    Neurophysiology 128.3 (2017): e18;-   Argento, Emanuele, et al. “Augmented Cognition via Brainwave    Entrainment in Virtual Reality: An Open, Integrated Brain    Augmentation in a Neuroscience System Approach.” Augmented Human    Research 2.1 (2017): 3;-   Bello, Nicholas P. “Altering Cognitive and Brain States Through    Cortical Entrainment” (2014); Costa-Faidella, Jordi, Elyse S.    Sussman, and Carles Escera. “Selective entrainment of brain    oscillations drives auditory perceptual organization.” NeuroImage    (2017);-   Börgers, Chrstoph. “Entrainment by Excitatory Input Pulses.” An    Introduction to Modeling Neuronal Dynamics. Springer International    Publishing, 2017. 183-192;-   Calderone, Daniel J., et al. “Entrainment of neural oscillations as    a modifiable substrate of attention.” Trends in cognitive sciences    18.6 (2014): 300-309;-   Casciaro, Francesco, et al. “Alpha-rhythm stimulation using brain    entrainment enhances heart rate variability in subjects with reduced    HRV.” World J. Neuroscience 3.04 (2013): 213;-   Chang, Daniel Wonchul. “Method and system for brain entertainment”    U.S. Pat. No. 8,636,640. 28 Jan. 2014; Colzato, Lorenza S.,    Amengual, Julia L., et al. “Local entrainment of oscillatory    activity induced by direct brain stimulation in humans.” Scientific    Reports 7 (2017);-   Conte, Elio, et al. “A Fast Fourier Transform analysis of time    series data of heart rate variability during alfa-rhythm stimulation    in brain entrainment” NeuroQuantology 11.3 (2013);-   Dikker, Suzanne, et al. “Brain-to-brain synchrony tracks real-world    dynamic group interactions in the classroom.” Current Biology 27.9    (2017): 1375-1380;-   Ding, Nai, and Jonathan Z. Simon. “Cortical entrainment to    continuous speech: functional roles and interpretations.” Frontiers    in human neuroscience 8 (2014);-   Doherty, Cormac. “A comparison of alpha brainwave entrainment, with    and without musical accompaniment” (2014);-   Falk, Simone, Cosima Lanzilotti, and Daniele Schön. “Tuning neural    phase entrainment to speech.” J. Cognitive Neuroscience (2017);-   Gao, Junling, et al. “Entrainment of chaotic activities in brain and    heart during MBSR mindfulness training.” Neuroscience letters 616    (2016): 218-223;-   Gooding-Williams, Gerard, Hongfang Wang, and Klaus Kessler.    “THETA-Rhythm Makes the World Go Round: Dissociative Effects of TMS    Theta Versus Alpha Entrainment of Right pTPJ on Embodied Perspective    Transformations.” Brain Topography (2017): 1-4;-   Hanslmayr, Simon, Jonas Matuschek, and Marie-Christin Fellner.    “Entrainment of prefrontal beta oscillations induces an endogenous    echo and impairs memory formation.” Current Biology 24.8 (2014):    904-909;-   Heideman, Simone G., Erik S. te Woerd, and Peter Praamstra.    “Rhythmic entrainment of slow brain activity preceding leg    movements.” Clin. Neurophysiology 126.2 (2015): 348-355;-   Helfrich, Randolph F., et al. “Entrainment of brain oscillations by    transcranial alternating current stimulation.” Current Biology 24.3    (2014): 333-339;-   Henry, Molly J., et al. “Aging affects the balance of neural    entrainment and top-down neural modulation in the listening brain.”    Nature Communications 8 (2017): ncomms15801;-   Horr, Ninja K., Maria Wimber, and Massimiliano Di Luca. “Perceived    time and temporal structure: Neural entrainment to isochronous    stimulation increases duration estimates.” Neuroimage 132 (2016):    148-156;-   Irwin, Rosie. “Entraining Brain Oscillations to Influence Facial    Perception.” (2015);-   Kalyan, Ritu, and Bipan Kaushal. “Binaural Entrainment and Its    Effects on Memory.” (2016);-   Keitel, Anne, et al. “Auditory cortical delta-entrainment interacts    with oscillatory power in multiple fronto-parietal networks.”    NeuroImage 147 (2017): 32-42;-   Keitel, Christian, Cliodhna Quigley, and Philipp Ruhnau.    “Stimulus-driven brain oscillations in the alpha range: entrainment    of intrinsic rhythms or frequency-following response?” J.    Neuroscience 34.31 (2014): 10137-10140;-   Koelsch, Stefan. “Music-evoked emotions: principles, brain    correlates, and implications for therapy.” Annals of the New York    Academy of Sciences 1337.1 (2015): 193-201;-   Kisem, Anne, et al. “Neural entrainment reflects temporal    predictions guiding speech comprehension.” the Eighth Annual Meeting    of the Society for the Neurobiology of Language (SNL 2016). 2016;-   Lee, Daniel Keewoong, Dongyeup Daniel Synn, and Daniel Chesong Lee.    “Intelligent earplug system.” U.S. patent application Ser. No.    15/106,989;-   Lefoumour, Joseph, Ramaswamy Palaniappan, and Ian V. McLoughlin.    “Inter-hemispheric and spectral power analyses of binaural beat    effects on the brain.” Matters 2.9 (2016): e201607000001;-   Mai, Guangting, James W. Minett, and William S-Y. Wang. “Delta,    theta, beta, and gamma brain oscillations index levels of auditory    sentence processing.” Neuroimage 133(2016):516-528;-   Marconi, Pier Luigi, et al. “The phase amplitude coupling to assess    brain network system integration.” Medical Measurements and    Applications (MeMeA), 2016 IEEE International Symposium on. IEEE,    2016;-   McLaren, Elgin-Skye, and Alissa N. Antle. “Exploring and Evaluating    Sound for Helping Children Self-Regulate with a Brain-Computer    Application.” Proceedings of the 2017 Conference on Interaction    Design and Children. ACM, 2017;-   Moisa, Marius, et al. “Brain network mechanisms underlying motor    enhancement by transcranial entrainment of gamma oscillations.” J.    Neuroscience 36.47 (2016): 12053-12065;-   Molinaro, Nicola, et al. “Out-of-synchrony speech entrainment in    developmental dyslexia.” Human brain mapping 37.8 (2016): 2767-2783;-   Moseley, Ralph. “Immersive brain entrainment in virtual worlds:    actualizing meditative states.” Emerging Trends and Advanced    Technologies for Computational Intelligence. Springer International    Publishing, 2016. 315-346;-   Neuling, Toraf, et al. “Friends, not foes: magnetoencephalography as    a tool to uncover brain dynamics during transcranial alternating    current stimulation.” Neuroimage 118 (2015): 406-413;-   Notbohm, Annika, Jurgen Kurths, and Christoph S. Herrmann.    “Modification of brain oscillations via rhythmic light stimulation    provides evidence for entrainment but not for superposition of    event-related responses.” Frontiers in human neuroscience 10 (2016);-   Nozaradan, S., et al. “P943: Neural entrainment to musical rhythms    in the human auditory cortex, as revealed by intracerebral    recordings.” Clin. Neurophysiology 125 (2014): S299;-   Palaniappan, Ramaswamy, et al. “Improving the feature stability and    classification performance of bimodal brain and heart biometrics.”    Advances in Signal Processing and Intelligent Recognition Systems.    Springer, Cham, 2016. 175-186;-   Palaniappan, Ramaswamy, Somnuk Phon-Amnuaisuk, and Chikkannan    Eswaran. “On the binaural brain entrainment indicating lower heart    rate variability.” Int J. Cardiol 190 (2015): 262-263;-   Papagiannakis, G., et al. A virtual reality brainwave entrainment    method for human augmentation applications. Technical Report,    FORTH-ICS/TR-458, 2015;-   Park, Hyojin, et al. “Frontal top-down signals increase coupling of    auditory low-frequency oscillations to continuous speech in human    listeners.” Current Biology 25.12 (2015): 1649-1653;-   Pérez, Alejandro, Manuel Carreiras, and Jon Andoni Duñabeitia.    “Brain-to-brain entrainment EEG interbrain synchronization while    speaking and listening.” Scientific Reports 7 (2017);-   Riecke, Lars, Alexander T. Sack, and Charles E. Schroeder.    “Endogenous delta/theta sound-brain phase entrainment accelerates    the buildup of auditory streaming.” Current Biology 25.24 (2015):    3196-3201;-   Spaak, Eelke, Floris P. de Lange, and Ole Jensen. “Local entrainment    of alpha oscillations by visual stimuli causes cyclic modulation of    perception.” J. Neuroscience 34.10(2014):3536-3544;-   Thaut, Michael H. “The discovery of human auditory-motor entrainment    and its role in the development of neurologic music therapy.”    Progress in brain research 217 (2015): 253-266;-   Thaut, Michael H., Gerald C. McIntosh, and Volker Hoemberg.    “Neurobiological foundations of neurologic music therapy, rhythmic    entrainment and the motor system.” Frontiers in psychology 5 (2014);-   Thut, G. “7030 Guiding TMS by EEG/MEG to interact with oscillatory    brain activity and associated functions.” Clin. Neurophysiology    128.3 (2017): e9;-   Treviño, Guadalupe Villarreal, et al. “The Effect of Audio Visual    Entrainment on Pre-Attentive Dysfunctional Processing to Stressful    Events in Anxious Individuals.” Open J. Medical Psychology 3.05    (2014): 364;-   Trost, Wiebke, et al. “Getting the beat entrainment of brain    activity by musical rhythm and pleasantness.” NeuroImage 103 (2014):    55-64;-   Tsai, Shu-Hui, and Yue-Der Lin. “Autonomie feedback with brain    entrainment” Awareness Science and Technology and Ubi-Media    Computing (iCAST-UMEDIA), 2013 International Joint Conference on.    IEEE, 2013;-   Vossen, Alexandra, Joachim Gross, and Gregor Thut “Alpha power    increase after transcranial alternating current stimulation at alpha    frequency (α-tACS) reflects plastic changes rather than entrainment”    Brain Stimulation 8.3 (2015): 499-508;-   Witkowski, Matthias, et al. “Mapping entrained brain oscillations    during transcranial alternating current stimulation (tACS).”    Neuroimage 140 (2016): 89-98;-   Ziotnik, Anatoly, Raphael Nagao, and István Z. Kiss Jr-Shin Li.    “Phase-selective entrainment of nonlinear oscillator ensembles.”    Nature Communications 7 (2016).

The entrainment hypothesis (Thut and Miniussi, 2009; Thut et al., 2011a,2012), suggests the possibility of inducing a particular oscillationfrequency in the brain using an external oscillatory force (e.g., rTMS,but also tACS). The physiological basis of oscillatory cortical activitylies in the timing of the interacting neurons; when groups of neuronssynchronize their firing activities, brain rhythms emerge, networkoscillations are generated, and the basis for interactions between brainareas may develop (Buzsàki, 2006). Because of the variety ofexperimental protocols for brain stimulation, limits on descriptions ofthe actual protocols employed, and limited controls, consistency ofreported studies is lacking, and extrapolability is limited. Thus, whilethere is various consensus in various aspects of the effects of extracranial brain stimulation, the results achieved have a degree ofuncertainty dependent on details of implementation. On the other hand,within a specific experimental protocol, i is possible to obtainstatistically significant and repeatable results. This implies thatfeedback control might be effective to control implementation of thestimulation for a given purpose; however, studies that employ feedbackcontrol are lacking.

Different cognitive states are associated with different oscillatorypatterns in the brain (Buzsàki, 2006; Canolty and Knight, 2010; Varelaet al., 2001). Thut et al. (2011 b) directly tested the entrainmenthypothesis by means of a concurrent EEG-TMS experiment They firstdetermined the individual source of the parietal-occipital alphamodulation and the individual alpha frequency (magnetoencephalographystudy). They then applied rTMS at the individual alpha power whilerecording the EEG activity at rest. The results confirmed the threepredictions of the entrainment hypothesis: the induction of a specificfrequency after TMS, the enhancement of oscillation during TMSstimulation due to synchronization, and a phase alignment of the inducedfrequency and the ongoing activity (Thut et al., 2011 b).

If associative stimulation is a general principle for human neuralplasticity in which the timing and strength of activation are criticalfactors, it is possible that synchronization within or between areasusing an external force to phase/align oscillations can also favorefficient communication and associative plasticity (or altercommunication). In this respect associative, cortico-corticalstimulation has been shown to enhance coherence of oscillatory activitybetween the stimulated areas (Plewnia et al., 2008).

In coherence resonance (Longtin, 1997), the addition of a certain amountof noise in an excitable system results in the most coherent andproficient oscillatory responses. The brain's response to externaltiming-embedded stimulation can result in a decrease in phase varianceand an enhanced alignment (clustering) of the phase components of theongoing EEG activity (entraining, phase resetting) that can change thesignal-to-noise ratio and increase (or decrease) signal efficacy.

If one considers neuron activity within the brain as a set of looselycoupled oscillators, then the various parameters that might becontrolled include the size of the region of neurons, frequency ofoscillation, resonant frequency or time-constant, oscillator damping,noise, amplitude, coupling to other oscillators, and of course, externalinfluences that may include stimulation and/or power loss. In a humanbrain, pharmacological intervention may be significant. For example,drugs that alter excitability, such as caffeine, neurotransmitterrelease and reuptake, nerve conductance, etc. can all influenceoperation of the neural oscillators. Likewise, sub-threshold externalstimulation effects, including DC, AC and magnetic electromagneticeffects, can also influence operation of the neural oscillators.

Phase resetting or shifting can synchronize inputs and favorcommunication and, eventually, Hebbian plasticity (Hebb, 1949). Thus,rhythmic stimulation may induce a statistically higher degree ofcoherence in spiking neurons, which facilitates the induction of aspecific cognitive process (or hinders that process). Here, theperspective is slightly different (coherence resonance), but theunderlining mechanisms are similar to the ones described so far(stochastic resonance), and the additional key factor is the repetitionat a specific rhythm of the stimulation.

In the 1970's, the British biophysicist and psychobiologist, C. MaxwellCade, monitored the brainwave patterns of advanced meditators and 300 ofhis students. Here he found that the most advanced meditators have aspecific brainwave pattern that was different from the rest of hisstudents. He noted that these meditators showed high activity of alphabrainwaves accompanied by beta, theta and even delta waves that wereabout half the amplitude of the alpha waves. See, Cade “The AwakenedMind: Biofeedback and the Development of Higher States of Awareness”(Dell, 1979). Anna Wise extended Cade's studies, and found thatextraordinary achievers which included composers, inventors, artists,athletes, dancers, scientists, mathematicians, CEO's and presidents oflarge corporations have brainwave patterns differ from averageperformers, with a specific balance between Beta, Alpha, Theta and Deltabrainwaves where Alpha had the strongest amplitude. See, Anna Wise, “TheHigh-Performance Mind: Mastering Brainwaves for Insight, Healing, andCreativity”.

Entrainment is plausible because of the characteristics of thedemonstrated EEG responses to a single TMS pulse, which have a spectralcomposition which resemble the spontaneous oscillations of thestimulated cortex. For example, TMS of the “testing” visual (Rosanova etal., 2009) or motor cortices (Veniero et al., 2011) triggersalpha-waves, the natural frequency at the resting state of both types ofcortices. With the entrainment hypothesis, the noise generationframework moves to a more complex and extended level in which noise issynchronized with on-going activity. Nevertheless, the model to explainthe outcome will not change, stimulation will interact with the system,and the final result will depend on introducing or modifying the noiselevel. The entrainment hypothesis makes clear predictions with respectto online repetitive TMS paradigms' frequency engagement as well as thepossibility of inducing phase alignment, i.e., a reset of ongoing brainoscillations via external spTMS (Thut et al., 2011a, 2012; Veniero etal., 2011). The entrainment hypothesis is superior to the localizationapproach in gaining knowledge about how the brain works, rather thanwhere or when a single process occurs. TMS pulses may phase-align thenatural, ongoing oscillation of the target cortex. When additional TMSpulses are delivered in synchrony with the phase-aligned oscillation(i.e., at the same frequency), further synchronized phase-alignment willoccur, which will bring the oscillation of the target area in resonancewith the TMS train. Thus, entrainment may be expected when TMS isfrequency-tuned to the underlying brain oscillations (Veniero et al.,2011).

Binaural Beats

Binaural beats are auditory brainstem responses which originate in thesuperior olivary nucleus of each hemisphere. They result from theinteraction of two different auditory impulses, originating in oppositeears, below 1000 Hz and which differ in frequency between one and 30 Hz.For example, if a pure tone of 400 Hz is presented to the right ear anda pure tone of 410 Hz is presented simultaneously to the left ear, anamplitude modulated standing wave of 10 Hz, the difference between thetwo tones, is experienced as the two wave forms mesh in and out of phasewithin the superior olivary nuclei. This binaural beat is not heard inthe ordinary sense of the word (the human range of hearing is from20-20,000 Hz). It is perceived as an auditory beat and theoretically canbe used to entrain specific neural rhythms through thefrequency-following response (FFR)—the tendency for cortical potentialsto entrain to or resonate at the frequency of an external stimulus.Thus, it is theoretically possible to utilize a specific binaural-beatfrequency as a consciousness management technique to entrain a specificcortical rhythm. The binaural-beat appears to be associated with anelectroencephalographic (EEG) frequency-following response in the brain.

Uses of audio with embedded binaural beats that are mixed with music orvarious pink or background sound are diverse. They range fromrelaxation, meditation, stress reduction, pain management, improvedsleep quality, decrease in sleep requirements, super learning, enhancedcreativity and intuition, remote viewing, telepathy, and out-of-bodyexperience and lucid dreaming. Audio embedded with binaural beats isoften combined with various meditation techniques, as well as positiveaffirmations and visualization.

When signals of two different frequencies are presented, one to eachear, the brain detects phase differences between these signals. “Undernatural circumstances a detected phase difference would providedirectional information. The brain processes this anomalous informationdifferently when these phase differences are heard with stereoheadphones or speakers. A perceptual integration of the two signalstakes place, producing the sensation of a third “beat” frequency. Thedifference between the signals waxes and wanes as the two differentinput frequencies mesh in and out of phase. As a result of theseconstantly increasing and decreasing differences, an amplitude-modulatedstanding wave—the binaural beat—is heard. The binaural beat is perceivedas a fluctuating rhythm at the frequency of the difference between thetwo auditory inputs. Evidence suggests that the binaural beats aregenerated in the brainstem's superior olivary nucleus, the first site ofcontralateral integration in the auditory system. Studies also suggestthat the frequency-following response originates from the inferiorcolliculus. This activity is conducted to the cortex where it can berecorded by scalp electrodes. Binaural beats can easily be heard at thelow frequencies (<30 Hz) that are characteristic of the EEG spectrum.

Synchronized brainwaves have long been associated with meditative andhypnogogic states, and audio with embedded binaural beats has theability to induce and improve such states of consciousness. The reasonfor this is physiological. Each ear is “hardwired” (so to speak) to bothhemispheres of the brain. Each hemisphere has its own olivary nucleus(sound-processing center) which receives signals from each ear. Inkeeping with this physiological structure, when a binaural beat isperceived there are actually two standing waves of equal amplitude andfrequency present, one in each hemisphere. So, there are two separatestanding waves entraining portions of each hemisphere to the samefrequency. The binaural beats appear to contibute to the hemisphericsynchronization evidenced in meditative and hypnogogic states ofconsciousness. Brain function is also enhanced through the increase ofcross-collosal communication between the left and right hemispheres ofthe brain. en.wikipedia.org/wiki/Beat_(acoustics)#Binaural_beats. See:

-   Oster, G (October 1973). “Auditory beats in the brain”. Scientific    American. 229 (4): 94-102. See:-   Lane, J. D., Kasian, S. J., Owens, J. E., & Marsh, G. R. (1998).    Binaural auditory beats affect vigilance performance and mood.    Physiology & behavior, 63(2), 249-252;-   Foster, D. S. (1990). EEG and subjective correlates of alpha    frequency binaural beats stimulation combined with alpha biofeedback    (Doctoral dissertation, Memphis State University);-   Kasprzak, C. (2011). Influence of binaural beats on EEG signal. Acta    Physica Polonica A, 119(6A), 986-990;-   Pratt, H., Starr, A., Michalewski, H. J., Dimitijevic, A., Bleich,    N., & Mittelman, N. (2009). Cortical evoked potentials to an    auditory illusion: binaural beats. Clinical Neurophysiology, 120(8),    1514-1524;-   Pratt, H., Starr, A., Michalewski, H. J., Dimitijevic, A., Bleich,    N., & Mittelman, N. (2010). A comparison of auditory evoked    potentials to acoustic beats and to binaural beats. Hearing    research, 262(1), 34-44;-   Padmanabhan, R., Hildreth, A. J., & Laws, D. (2005). A prospective,    randomised, controlled study examining binaural beat audio and    pre-operative anxiety in patients undergoing general anaesthesia for    day case surgery. Anaesthesia, 60(9), 874-877;-   Reedijk, S. A., Bolders, A., & Hommel, B. (2013). The impact of    binaural beats on creativity. Frontiers in human neuroscience, 7;-   Atwater, F. H. (2001). Binaural beats and the regulation of arousal    levels. Proceedings of the TANS, 11;-   Hink, R. F., Kodera, K., Yamada, O., Kaga, K., & Suzuki, J. (1980).    Binaural interaction of a beating frequency-following response.    Audiology, 19(1), 36-43;-   Gao, X., Cao, H., Ming, D., Qi, H., Wang, X., Wang, X., & Zhou, P.    (2014). Analysis of EEG activity in response to binaural beats with    different frequencies. International Journal of Psychophysiology,    94(3), 399-406;-   Sung, H. C., Lee, W. L., Li, H. M., Lin, C. Y., Wu, Y. Z., Wang, J.    J., & Li, T. L. (2017). Familiar Music Listening with Binaural Beats    for Older People with Depressive Symptoms in Retirement Homes.    Neuropsychiatry, 7(4);-   Colzato, L. S., Barone, H., Sellaro, R., & Hommel, B. (2017). More    attentional focusing through binaural beats: evidence from the    global-local task. Psychological research, 81(1), 271-277;-   Mortazavi, S. M. J., Zahraei-Moghadam, S. M., Masoumi, S., Rafati,    A., Haghani, M., Mortazavi, S. A. R., & Zehtabian, M. (2017). Short    Term Exposure to Binaural Beats Adversely Affects Learning and    Memory in Rats. Journal of Biomedical Physics and Engineering.-   Brain Entrainment Frequency Following Response (or FFR). See,    “Stimulating the Brain with Light and Sound,” Transparent    Corporation, Neuroprogrammer™ 3,    www.transparentcorp.com/products/np/entrainmentphp.

Isochronic Tones

Isochronic tones are regular beats of a single tone that are usedalongside monaural beats and binaural beats in the process calledbrainwave entrainment. At its simplest level, an isochronic tone is atone that is being turned on and off rapidly. They create sharp,distinctive pulses of sound.

-   www.livingflow.net/isochronic-tones-work/;-   Schulze, H. H. (1989). The perception of temporal deviations in    isochronic patterns. Attention, Perception, & Psychophysics, 45(4),    291-296;-   Oster, G. (1973). Auditory beats in the brain. Scientific American,    229(4), 94-102;-   Huang, T. L., & Charyton, C. (2008). A comprehensive review of the    psychological effects of brainwave entrainment Alternative therapies    in health and medicine, 14(5), 38;-   Trost, W., Frihholz, S., Schon, D., Labbe, C., Pichon, S.,    Grandjean, D., & Vuilleumier, P. (2014). Getting the beat    entrainment of brain activity by musical rhythm and pleasantness.    NeuroImage, 103, 55-64;-   Casciaro, F., Laterza, V., Conte, S., Pieralice, M., Federici, A,    Todarello, O., . . . & Conte, E. (2013). Alpha-rhythm stimulation    using brain entrainment enhances heart rate variability in subjects    with reduced HRV. Word Journal of Neuroscience, 3(04), 213;-   Conte, Elio, Sergio Conte, Nunzia Santacroce, Antonio Federici,    Orlando Todarello, Franco Orsucci, Francesco Casciaro, and Vincenza    Laterza. “A Fast Fourier Transform analysis of time series data of    heart rate variability during alfa-rhythm stimulation in brain    entrainment” NeuroQuantology 11, no. 3 (2013);-   Doherty, C. (2014). A comparison of alpha brainwave entrainment with    and without musical accompaniment-   Moseley, R. (2015, July). Inducing targeted brain states utilizing    merged reality systems. In Science and Information Conference (SAI),    2015 (pp. 657-663). IEEE.

Time-Frequency Analysis

Brian J. Roach and Daniel H. Mathalon, “Event-related EEG time-frequencyanalysis: an overview of measures and analysis of early gamma band phaselocking in schizophrenia. Schizophrenia Bull. USA. 2008; 34:5:907-926.,describes a mechanism for EEG time-frequency analysis. Fourier andwavelet transforms (and their inverse) may be performed on EEG signals.

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 4,407,299; 4,408,616;4,421,122; 4,493,327; 4,550,736; 4,557,270; 4,579,125; 4,583,190;4,585,011; 4,610,259; 4,649,482; 4,705,049; 4,736,307; 4,744,029;4,776,345; 4,792,145; 4,794,533; 4,846,190; 4,862,359; 4,883,067;4,907,597; 4,924,875; 4,940,058; 5,010,891; 5,020,540; 5,029,082;5,083,571; 5,092,341; 5,105,354; 5,109,862; 5,218,530; 5,230,344;5,230,346; 5,233,517; 5,241,967; 5,243,517; 5,269,315; 5,280,791;5,287,859; 5,309,917; 5,309,923; 5,320,109; 5,339,811; 5,339,826;5,377,100; 5,406,956; 5,406,957; 5,443,073; 5,447,166; 5,458,117;5,474,082; 5,555,889; 5,611,350; 5,619,995; 5,632,272; 5,643,325;5,678,561; 5,685,313; 5,692,517; 5,694,939; 5,699,808; 5,752,521;5,755,739; 5,771,261; 5,771,897; 5,794,623; 5,795,304; 5,797,840;5,810,737; 5,813,993; 5,827,195; 5,840,040; 5,846,189; 5,846,208;5,853,005; 5,871,517; 5,884,626; 5,899,867; 5,916,171; 5,995,868;6,002,952; 6,011,990; 6,016,444; 6,021,345; 6,032,072; 6,044,292;6,050,940; 6,052,619; 6,067,462; 6,067,467; 6,070,098; 6,071,246;6,081,735; 6,097,980; 6,097,981; 6,115,631; 6,117,075; 6,129,681;6,155,993; 6,157,850; 6,157,857; 6,171,258; 6,195,576; 6,196,972;6,224,549; 6,236,872; 6,287,328; 6,292,688; 6,293,904; 6,305,943;6,306,077; 6,309,342; 6,315,736; 6,317,627; 6,325,761; 6,331,164;6,338,713; 6,343,229; 6,358,201; 6,366,813; 6,370,423; 6,375,614;6,377,833; 6,385,486; 6,394,963; 6,402,520; 6,475,163; 6,482,165;6,493,577; 6,496,724; 6,511,424; 6,520,905; 6,520,921; 6,524,249;6,527,730; 6,529,773; 6,544,170; 6,546,378; 6,547,736; 6,547,746;6,549,804; 6,556,861; 6,565,518; 6,574,573; 6,594,524; 6,602,202;6,616,611; 6,622,036; 6,625,485; 6,626,676; 6,650,917; 6,652,470;6,654,632; 6,658,287; 6,678,548; 6,687,525; 6,699,194; 6,709,399;6,726,624; 6,731,975; 6,735,467; 6,743,182; 6,745,060; 6,745,156;6,746,409; 6,751,499; 6,768,920; 6,798,898; 6,801,803; 6,804,661;6,816,744; 6,819,956; 6,826,426; 6,843,774; 6,865,494; 6,875,174;6,882,881; 6,886,964; 6,915,241; 6,928,354; 6,931,274; 6,931,275;6,981,947; 6,985,769; 6,988,056; 6,993,380; 7,011,410; 7,014,613;7,016,722; 7,037,260; 7,043,293; 7,054,454; 7,089,927; 7,092,748;7,099,714; 7,104,963; 7,105,824; 7,123,955; 7,128,713; 7,130,691;7,146,218; 7,150,710; 7,150,715; 7,150,718; 7,163,512; 7,164,941;7,177,675; 7,190,995; 7,207,948; 7,209,788; 7,215,986; 7,225,013;7,228,169; 7,228,171; 7,231,245; 7,254,433; 7,254,439; 7,254,500;7,267,652; 7,269,456; 7,286,871; 7,288,066; 7,297,110; 7,299,088;7,324,845; 7,328,053; 7,333,619; 7,333,851; 7,343,198; 7,367,949;7,373,198; 7,376,453; 7,381,185; 7,383,070; 7,392,079; 7,395,292;7,396,333; 7,399,282; 7,403,814; 7,403,815; 7,418,290; 7,429,247;7,450,986; 7,454,240; 7,462,151; 7,468,040; 7,469,697; 7,471,971;7,471,978; 7,489,958; 7,489,964; 7,491,173; 7,496,393; 7,499,741;7,499,745; 7,509,154; 7,509,161; 7,509,163; 7,510,531; 7,530,955;7,537,568; 7,539,532; 7,539,533; 7,547,284; 7,558,622; 7,559,903;7,570,991; 7,572,225; 7,574,007; 7,574,254; 7,593,767; 7,594,122;7,596,535; 7,603,168; 7,604,603; 7,610,094; 7,623,912; 7,623,928;7,625,340; 7,630,757; 7,640,055; 7,643,655; 7,647,098; 7,654,948;7,668,579; 7,668,591; 7,672,717; 7,676,263; 7,678,061; 7,684,856;7,697,979; 7,702,502; 7,706,871; 7,706,992; 7,711,417; 7,715,910;7,720,530; 7,727,161; 7,729,753; 7,733,224; 7,734,334; 7,747,325;7,751,878; 7,754,190; 7,757,690; 7,758,503; 7,764,987; 7,771,364;7,774,052; 7,774,064; 7,778,693; 7,787,946; 7,794,406; 7,801,592;7,801,593; 7,803,118; 7,803,119; 7,809,433; 7,811,279; 7,819,812;7,831,302; 7,853,329; 7,860,561; 7,865,234; 7,865,235; 7,878,965;7,879,043; 7,887,493; 7,894,890; 7,896,807; 7,899,525; 7,904,144;7,907,994; 7,909,771; 7,918,779; 7,920,914; 7,930,035; 7,938,782;7,938,785; 7,941,209; 7,942,824; 7,944,551; 7,962,204; 7,974,696;7,983,741; 7,983,757; 7,986,991; 7,993,279; 7,996,075; 8,002,553;8,005,534; 8,005,624; 8,010,347; 8,019,400; 8,019,410; 8,024,032;8,025,404; 8,032,209; 8,033,996; 8,036,728; 8,036,736; 8,041,136;8,046,041; 8,046,042; 8,065,011; 8,066,637; 8,066,647; 8,068,904;8,073,534; 8,075,499; 8,079,953; 8,082,031; 8,086,294; 8,089,283;8,095,210; 8,103,333; 8,108,036; 8,108,039; 8,114,021; 8,121,673;8,126,528; 8,128,572; 8,131,354; 8,133,172; 8,137,269; 8,137,270;8,145,310; 8,152,732; 8,155,736; 8,160,689; 8,172,766; 8,177,726;8,177,727; 8,180,420; 8,180,601; 8,185,207; 8,187,201; 8,190,227;8,190,249; 8,190,251; 8,197,395; 8,197,437; 8,200,319; 8,204,583;8,211,035; 8,214,007; 8,224,433; 8,236,005; 8,239,014; 8,241,213;8,244,340; 8,244,475; 8,249,698; 8,271,077; 8,280,502; 8,280,503;8,280,514; 8,285,368; 8,290,575; 8,295,914; 8,296,108; 8,298,140;8,301,232; 8,301,233; 8,306,610; 8,311,622; 8,314,707; 8,315,970;8,320,649; 8,323,188; 8,323,189; 8,323,204; 8,328,718; 8,332,017;8,332,024; 8,335,561; 8,337,404; 8,340,752; 8,340,753; 8,343,026;8,346,342; 8,346,349; 8,352,023; 8,353,837; 8,354,881; 8,356,594;8,359,080; 8,364,226; 8,364,254; 8,364,255; 8,369,940; 8,374,690;8,374,703; 8,380,296; 8,382,667; 8,386,244; 8,391,966; 8,396,546;8,396,557; 8,401,624; 8,401,626; 8,403,848; 8,425,415; 8,425,583;8,428,696; 8,437,843; 8,437,844; 8,442,626; 8,449,471; 8,452,544;8,454,555; 8,461,988; 8,463,007; 8,463,349; 8,463,370; 8,465,408;8,467,877; 8,473,024; 8,473,044; 8,473,306; 8,475,354; 8,475,368;8,475,387; 8,478,389; 8,478,394; 8,478,402; 8,480,554; 8,484,270;8,494,829; 8,498,697; 8,500,282; 8,500,636; 8,509,885; 8,509,904;8,512,221; 8,512,240; 8,515,535; 8,519,853; 8,521,284; 8,525,673;8,525,687; 8,527,435; 8,531,291; 8,538,512; 8,538,514; 8,538,705;8,542,900; 8,543,199; 8,543,219; 8,545,416; 8,545,436; 8,554,311;8,554,325; 8,560,034; 8,560,073; 8,562,525; 8,562,526; 8,562,527;8,562,951; 8,568,329; 8,571,642; 8,585,568; 8,588,933; 8,591,419;8,591,498; 8,597,193; 8,600,502; 8,606,351; 8,606,356; 8,606,360;8,620,419; 8,628,480; 8,630,699; 8,632,465; 8,632,750; 8,641,632;8,644,914; 8,644,921; 8,647,278; 8,649,866; 8,652,038; 8,655,817;8,657,756; 8,660,799; 8,666,467; 8,670,603; 8,672,852; 8,680,991;8,684,900; 8,684,922; 8,684,926; 8,688,209; 8,690,748; 8,693,756;8,694,087; 8,694,089; 8,694,107; 8,700,137; 8,700,141; 8,700,142;8,706,205; 8,706,206; 8,706,207; 8,708,903; 8,712,507; 8,712,513;8,725,238; 8,725,243; 8,725,311; 8,725,669; 8,727,978; 8,728,001;8,738,121; 8,744,563; 8,747,313; 8,747,336; 8,750,971; 8,750,974;8,750,992; 8,755,854; 8,755,856; 8,755,868; 8,755,869; 8,755,871;8,761,866; 8,761,869; 8,764,651; 8,764,652; 8,764,653; 8,768,447;8,771,194; 8,775,340; 8,781,193; 8,781,563; 8,781,595; 8,781,597;8,784,322; 8,786,624; 8,790,255; 8,790,272; 8,792,974; 8,798,735;8,798,736; 8,801,620; 8,821,408; 8,825,149; 8,825,428; 8,827,917;8,831,705; 8,838,226; 8,838,227; 8,843,199; 8,843,210; 8,849,390;8,849,392; 8,849,681; 8,852,100; 8,852,103; 8,855,758; 8,858,440;8,858,449; 8,862,196; 8,862,210; 8,862,581; 8,868,148; 8,868,163;8,868,172; 8,868,174; 8,868,175; 8,870,737; 8,880,207; 8,880,576;8,886,299; 8,888,672; 8,888,673; 8,888,702; 8,888,708; 8,898,037;8,902,070; 8,903,483; 8,914,100; 8,915,741; 8,915,871; 8,918,162;8,918,178; 8,922,788; 8,923,958; 8,924,235; 8,932,227; 8,938,301;8,942,777; 8,948,834; 8,948,860; 8,954,146; 8,958,882; 8,961,386;8,965,492; 8,968,195; 8,977,362; 8,983,591; 8,983,628; 8,983,629;8,986,207; 8,989,835; 8,989,836; 8,996,112; 9,008,367; 9,008,754;9,008,771; 9,014,216; 9,014,453; 9,014,819; 9,015,057; 9,020,576;9,020,585; 9,020,789; 9,022,936; 9,026,202; 9,028,405; 9,028,412;9,033,884; 9,037,224; 9,037,225; 9,037,530; 9,042,952; 9,042,958;9,044,188; 9,055,871; 9,058,473; 9,060,671; 9,060,683; 9,060,695;9,060,722; 9,060,746; 9,072,482; 9,078,577; 9,084,584; 9,089,310;9,089,400; 9,095,266; 9,095,268; 9,100,758; 9,107,586; 9,107,595;9,113,777; 9,113,801; 9,113,830; 9,116,835; 9,119,551; 9,119,583;9,119,597; 9,119,598; 9,125,574; 9,131,864; 9,135,221; 9,138,183;9,149,214; 9,149,226; 9,149,255; 9,149,577; 9,155,484; 9,155,487;9,155,521; 9,165,472; 9,173,582; 9,173,610; 9,179,854; 9,179,876;9,183,351 RE34015; RE38476; RE38749; RE46189; 20010049480; 20010051774;20020035338; 20020055675; 20020059159; 20020077536; 20020082513;20020085174; 20020091319; 20020091335; 20020099295; 20020099306;20020103512; 20020107454; 20020112732; 20020117176; 20020128544;20020138013; 20020151771; 20020177882; 20020182574; 20020183644;20020193670; 20030001098; 20030009078; 20030023183; 20030028121;20030032888; 20030035301; 20030036689; 20030046018; 20030055355;20030070685; 20030093004; 20030093129; 20030100844; 20030120172;20030130709; 20030135128; 20030139681; 20030144601; 20030149678;20030158466; 20030158496; 20030158587; 20030160622; 20030167019;20030171658; 20030171685; 20030176804; 20030181821; 20030185408;20030195429; 20030216654; 20030225340; 20030229291; 20030236458;20040002635; 20040006265; 20040006376; 20040010203; 20040039268;20040059203; 20040059241; 20040064020; 20040064066; 20040068164;20040068199; 20040073098; 20040073129; 20040077967; 20040079372;20040082862; 20040082876; 20040097802; 20040116784; 20040116791;20040116798; 20040116825; 20040117098; 20040143170; 20040144925;20040152995; 20040158300; 20040167418; 20040181162; 20040193068;20040199482; 20040204636; 20040204637; 20040204659; 20040210146;20040220494; 20040220782; 20040225179; 20040230105; 20040243017;20040254493; 20040260169; 20050007091; 20050010116; 20050018858;20050025704; 20050033154; 20050033174; 20050038354; 20050043774;20050075568; 20050080349; 20050080828; 20050085744; 20050096517;20050113713; 20050119586; 20050124848; 20050124863; 20050135102;20050137494; 20050148893; 20050148894; 20050148895; 20050149123;20050182456; 20050197590; 20050209517; 20050216071; 20050251055;20050256385; 20050256418; 20050267362; 20050273017; 20050277813;20050277912; 20060004298; 20060009704; 20060015034; 20060041201;20060047187; 20060047216; 20060047324; 20060058590; 20060074334;20060082727; 20060084877; 20060089541; 20060089549; 20060094968;20060100530; 20060102171; 20060111644; 20060116556; 20060135880;20060149144; 20060153396; 20060155206; 20060155207; 20060161071;20060161075; 20060161218; 20060167370; 20060167722; 20060173364;20060184059; 20060189880; 20060189882; 20060200016; 20060200034;20060200035; 20060204532; 20060206033; 20060217609; 20060233390;20060235315; 20060235324; 20060241562; 20060241718; 20060251303;20060258896; 20060258950; 20060265022; 20060276695; 20070007454;20070016095; 20070016264; 20070021673; 20070021675; 20070032733;20070032737; 20070038382; 20070060830; 20070060831; 20070066914;20070083128; 20070093721; 20070100246; 20070100251; 20070100666;20070129647; 20070135724; 20070135728; 20070142862; 20070142873;20070149860; 20070161919; 20070162086; 20070167694; 20070167853;20070167858; 20070167991; 20070173733; 20070179396; 20070191688;20070191691; 20070191697; 20070197930; 20070203448; 20070208212;20070208269; 20070213786; 20070225581; 20070225674; 20070225932;20070249918; 20070249952; 20070255135; 20070260151; 20070265508;20070265533; 20070273504; 20070276270; 20070276278; 20070276279;20070276609; 20070291832; 20080001600; 20080001735; 20080004514;20080004904; 20080009685; 20080009772; 20080013747; 20080021332;20080021336; 20080021340; 20080021342; 20080033266; 20080036752;20080045823; 20080045844; 20080051669; 20080051858; 20080058668;20080074307; 20080077010; 20080077015; 20080082018; 20080097197;20080119716; 20080119747; 20080119900; 20080125669; 20080139953;20080140403; 20080154111; 20080167535; 20080167540; 20080167569;20080177195; 20080177196; 20080177197; 20080188765; 20080195166;20080200831; 20080208072; 20080208073; 20080214902; 20080221400;20080221472; 20080221969; 20080228100; 20080242521; 20080243014;20080243017; 20080243021; 20080249430; 20080255469; 20080257349;20080260212; 20080262367; 20080262371; 20080275327; 20080294019;20080294063; 20080319326; 20080319505; 20090005675; 20090009284;20090018429; 20090024007; 20090030476; 20090043221; 20090048530;20090054788; 20090062660; 20090062670; 20090062676; 20090062679;20090062680; 20090062696; 20090076339; 20090076399; 20090076400;20090076407; 20090082689; 20090082690; 20090083071; 20090088658;20090094305; 20090112281; 20090118636; 20090124869; 20090124921;20090124922; 20090124923; 20090137915; 20090137923; 20090149148;20090156954; 20090156956; 20090157662; 20090171232; 20090171240;20090177090; 20090177108; 20090179642; 20090182211; 20090192394;20090198144; 20090198145; 20090204015; 20090209835; 20090216091;20090216146; 20090227876; 20090227877; 20090227882; 20090227889;20090240119; 20090247893; 20090247894; 20090264785; 20090264952;20090275853; 20090287107; 20090292180; 20090297000; 20090306534;20090312663; 20090312664; 20090312808; 20090312817; 20090316925;20090318779; 20090323049; 20090326353; 20100010364; 20100023089;20100030073; 20100036211; 20100036276; 20100041962; 20100042011;20100043795; 20100049069; 20100049075; 20100049482; 20100056939;20100069762; 20100069775; 20100076333; 20100076338; 20100079292;20100087900; 20100094103; 20100094152; 20100094155; 20100099954;20100106044; 20100114813; 20100130869; 20100137728; 20100137937;20100143256; 20100152621; 20100160737; 20100174161; 20100179447;20100185113; 20100191124; 20100191139; 20100191305; 20100195770;20100198098; 20100198101; 20100204614; 20100204748; 20100204750;20100217100; 20100217146; 20100217348; 20100222694; 20100224188;20100234705; 20100234752; 20100234753; 20100245093; 20100249627;20100249635; 20100258126; 20100261977; 20100262377; 20100268055;20100280403; 20100286549; 20100286747; 20100292752; 20100293115;20100298735; 20100303101; 20100312188; 20100318025; 20100324441;20100331649; 20100331715; 20110004115; 20110009715; 20110009729;20110009752; 20110015501; 20110015536; 20110028802; 20110028859;20110034822; 20110038515; 20110040202; 20110046473; 20110054279;20110054345; 20110066005; 20110066041; 20110066042; 20110066053;20110077538; 20110082381; 20110087125; 20110092834; 20110092839;20110098583; 20110105859; 20110105915; 20110105938; 20110106206;20110112379; 20110112381; 20110112426; 20110112427; 20110115624;20110118536; 20110118618; 20110118619; 20110119212; 20110125046;20110125048; 20110125238; 20110130675; 20110144520; 20110152710;20110160607; 20110160608; 20110160795; 20110162645; 20110178441;20110178581; 20110181422; 20110184650; 20110190600; 20110196693;20110208539; 20110218453; 20110218950; 20110224569; 20110224570;20110224602; 20110245709; 20110251583; 20110251985; 20110257517;20110263995; 20110270117; 20110270579; 20110282234; 20110288424;20110288431; 20110295142; 20110295143; 20110295338; 20110301436;20110301439; 20110301441; 20110301448; 20110301486; 20110301487;20110307029; 20110307079; 20110313308; 20110313760; 20110319724;20120004561; 20120004564; 20120004749; 20120010536; 20120016218;20120016252; 20120022336; 20120022350; 20120022351; 20120022365;20120022384; 20120022392; 20120022844; 20120029320; 20120029378;20120029379; 20120035431; 20120035433; 20120035765; 20120041330;20120046711; 20120053433; 20120053491; 20120059273; 20120065536;20120078115; 20120083700; 20120083701; 20120088987; 20120088992;20120089004; 20120092156; 20120092157; 20120095352; 20120095357;20120100514; 20120101387; 20120101401; 20120101402; 20120101430;20120108999; 20120116235; 20120123232; 20120123290; 20120125337;20120136242; 20120136605; 20120143074; 20120143075; 20120149997;20120150545; 20120157963; 20120159656; 20120165624; 20120165631;20120172682; 20120172689; 20120172743; 20120191000; 20120197092;20120197153; 20120203087; 20120203130; 20120203131; 20120203133;20120203725; 20120209126; 20120209136; 20120209139; 20120220843;20120220889; 20120221310; 20120226334; 20120238890; 20120242501;20120245464; 20120245481; 20120253141; 20120253219; 20120253249;20120265080; 20120271190; 20120277545; 20120277548; 20120277816;20120296182; 20120296569; 20120302842; 20120302845; 20120302856;20120302894; 20120310100; 20120310105; 20120321759; 20120323132;20120330109; 20130006124; 20130009783; 20130011819; 20130012786;20130012787; 20130012788; 20130012789; 20130012790; 20130012802;20130012830; 20130013327; 20130023783; 20130030257; 20130035579;20130039498; 20130041235; 20130046151; 20130046193; 20130046715;20130060110; 20130060125; 20130066392; 20130066394; 20130066395;20130069780; 20130070929; 20130072807; 20130076885; 20130079606;20130079621; 20130079647; 20130079656; 20130079657; 20130080127;20130080489; 20130095459; 20130096391; 20130096393; 20130096394;20130096408; 20130096441; 20130096839; 20130096840; 20130102833;20130102897; 20130109995; 20130109996; 20130116520; 20130116561;20130116588; 20130118494; 20130123584; 20130127708; 20130130799;20130137936; 20130137938; 20130138002; 20130144106; 20130144107;20130144108; 20130144183; 20130150650; 20130150651; 20130150659;20130159041; 20130165812; 20130172686; 20130172691; 20130172716;20130172763; 20130172767; 20130172772; 20130172774; 20130178718;20130182860; 20130184552; 20130184558; 20130184603; 20130188854;20130190577; 20130190642; 20130197321; 20130197322; 20130197328;20130197339; 20130204150; 20130211224; 20130211276; 20130211291;20130217982; 20130218043; 20130218053; 20130218233; 20130221961;20130225940; 20130225992; 20130231574; 20130231580; 20130231947;20130238049; 20130238050; 20130238063; 20130245422; 20130245486;20130245711; 20130245712; 20130266163; 20130267760; 20130267866;20130267928; 20130274580; 20130274625; 20130275159; 20130281811;20130282339; 20130289401; 20130289413; 20130289417; 20130289424;20130289433; 20130295016; 20130300573; 20130303828; 20130303934;20130304153; 20130310660; 20130310909; 20130324880; 20130338449;20130338459; 20130344465; 20130345522; 20130345523; 20140005988;20140012061; 20140012110; 20140012133; 20140012153; 20140018792;20140019165; 20140023999; 20140025396; 20140025397; 20140038147;20140046208; 20140051044; 20140051960; 20140051961; 20140052213;20140055284; 20140058241; 20140066739; 20140066763; 20140070958;20140072127; 20140072130; 20140073863; 20140073864; 20140073866;20140073870; 20140073875; 20140073876; 20140073877; 20140073878;20140073898; 20140073948; 20140073949; 20140073951; 20140073953;20140073954; 20140073955; 20140073956; 20140073960; 20140073961;20140073963; 20140073965; 20140073966; 20140073967; 20140073968;20140073974; 20140073975; 20140074060; 20140074179; 20140074180;20140077946; 20140081114; 20140081115; 20140094720; 20140098981;20140100467; 20140104059; 20140105436; 20140107464; 20140107519;20140107525; 20140114165; 20140114205; 20140121446; 20140121476;20140121554; 20140128762; 20140128764; 20140135879; 20140136585;20140140567; 20140143064; 20140148723; 20140152673; 20140155706;20140155714; 20140155730; 20140156000; 20140163328; 20140163330;20140163331; 20140163332; 20140163333; 20140163335; 20140163336;20140163337; 20140163385; 20140163409; 20140163425; 20140163897;20140171820; 20140175261; 20140176944; 20140179980; 20140180088;20140180092; 20140180093; 20140180094; 20140180095; 20140180096;20140180097; 20140180099; 20140180100; 20140180112; 20140180113;20140180145; 20140180153; 20140180160; 20140180161; 20140180176;20140180177; 20140180597; 20140187994; 20140188006; 20140188770;20140194702; 20140194758; 20140194759; 20140194768; 20140194769;20140194780; 20140194793; 20140203797; 20140213937; 20140214330;20140228651; 20140228702; 20140232516; 20140235965; 20140236039;20140236077; 20140237073; 20140243614; 20140243621; 20140243628;20140243694; 20140249429; 20140257073; 20140257147; 20140266696;20140266787; 20140275886; 20140275889; 20140275891; 20140276013;20140276014; 20140276090; 20140276123; 20140276130; 20140276181;20140276183; 20140279746; 20140288381; 20140288614; 20140288953;20140289172; 20140296724; 20140303453; 20140303454; 20140303508;20140309943; 20140313303; 20140316217; 20140316221; 20140316230;20140316235; 20140316278; 20140323900; 20140324118; 20140330102;20140330157; 20140330159; 20140330334; 20140330404; 20140336473;20140347491; 20140350431; 20140350436; 20140358025; 20140364721;20140364746; 20140369537; 20140371544; 20140371599; 20140378809;20140378810; 20140379620; 20150003698; 20150003699; 20150005592;20150005594; 20150005640; 20150005644; 20150005660; 20150005680;20150006186; 20150016618; 20150018758; 20150025351; 20150025422;20150032017; 20150038804; 20150038869; 20150039110; 20150042477;20150045686; 20150051663; 20150057512; 20150065839; 20150073237;20150073306; 20150080671; 20150080746; 20150087931; 20150088024;20150092949; 20150093729; 20150099941; 20150099962; 20150103360;20150105631; 20150105641; 20150105837; 20150112222; 20150112409;20150119652; 20150119743; 20150119746; 20150126821; 20150126845;20150126848; 20150126873; 20150134264; 20150137988; 20150141529;20150141789; 20150141794; 20150153477; 20150157235; 20150157266;20150164349; 20150164362; 20150164375; 20150164404; 20150181840;20150182417; 20150190070; 20150190085; 20150190636; 20150190637;20150196213; 20150199010; 20150201879; 20150202447; 20150203822;20150208940; 20150208975; 20150213191; 20150216436; 20150216468;20150217082; 20150220486; 20150223743; 20150227702; 20150230750;20150231408; 20150238106; 20150238112; 20150238137; 20150245800;20150247921; 20150250393; 20150250401; 20150250415; 20150257645;20150257673; 20150257674; 20150257700; 20150257712; 20150265164;20150269825; 20150272465; 20150282730; 20150282755; 20150282760;20150290420; 20150290453; 20150290454; 20150297106; 20150297141;20150304101; 20150305685; 20150309563; 20150313496; 20150313535;20150327813; 20150327837; 20150335292; 20150342478; 20150342493;20150351655; 20150351701; 20150359441; 20150359450; 20150359452;20150359467; 20150359486; 20150359492; 20150366497; 20150366504;20150366516; 20150366518; 20150374285; 20150374292; 20150374300;20150380009; 20160000348; 20160000354; 20160007915; 20160007918;20160012749; 20160015281; 20160015289; 20160022141; 20160022156;20160022164; 20160022167; 20160022206; 20160027293; 20160029917;20160029918; 20160029946; 20160029950; 20160029965; 20160030702;20160038037; 20160038038; 20160038049; 20160038091; 20160045150;20160045756; 20160051161; 20160051162; 20160051187; 20160051195;20160055415; 20160058301; 20160066788; 20160067494; 20160073886;20160074661; 20160081577; 20160081616; 20160087603; 20160089031;20160100769; 20160101260; 20160106331; 20160106344; 20160112022;20160112684; 20160113539; 20160113545; 20160113567; 20160113587;20160119726; 20160120433; 20160120434; 20160120464; 20160120480;20160128596; 20160132654; 20160135691; 20160135727; 20160135754;20160140834; 20160143554; 20160143560; 20160143594; 20160148531;20160150988; 20160151014; 20160151018; 20160151628; 20160157742;20160157828; 20160162652; 20160165852; 20160165853; 20160166169;20160166197; 20160166199; 20160166208; 20160174099; 20160174863;20160178392; 20160183828; 20160183861; 20160191517; 20160192841;20160192842; 20160192847; 20160192879; 20160196758; 20160198963;20160198966; 20160202755; 20160206877; 20160206880; 20160213276;20160213314; 20160220133; 20160220134; 20160220136; 20160220166;20160220836; 20160220837; 20160224757; 20160228019; 20160228029;20160228059; 20160228705; 20160232811; 20160235324; 20160235351;20160235352; 20160239084; 20160242659; 20160242690; 20160242699;20160248434; 20160249841; 20160256063; 20160256112; 20160256118;20160259905; 20160262664; 20160262685; 20160262695; 20160262703;20160278651; 20160278697; 20160278713; 20160282941; 20160287120;20160287157; 20160287162; 20160287166; 20160287871; 20160296157;20160302683; 20160302704; 20160302709; 20160302720; 20160302737;20160303402; 20160310031; 20160310070; 20160317056; 20160324465;20160331264; 20160338634; 20160338644; 20160338798; 20160346542;20160354003; 20160354027; 20160360965; 20160360970; 20160361021;20160361041; 20160367204; 20160374581; 20160374618; 20170000404;20170001016; 20170007165; 20170007173; 20170014037; 20170014083;20170020434; 20170020447; 20170027467; 20170032098; 20170035392;20170042430; 20170042469; 20170042475; 20170053513; 20170055839;20170055898; 20170055913; 20170065199; 20170065218; 20170065229;20170071495; 20170071523; 20170071529; 20170071532; 20170071537;20170071546; 20170071551; 20170071552; 20170079538; 20170079596;20170086672; 20170086695; 20170091567; 20170095721; 20170105647;20170112379; 20170112427; 20170120066; 20170127946; 20170132816;20170135597; 20170135604; 20170135626; 20170135629; 20170135631;20170135633; 20170143231; 20170143249; 20170143255; 20170143257;20170143259; 20170143266; 20170143267; 20170143268; 20170143273;20170143280; 20170143282; 20170143960; 20170143963; 20170146386;20170146387; 20170146390; 20170146391; 20170147754; 20170148240;20170150896; 20170150916; 20170156593; 20170156606; 20170156655;20170164878; 20170164901; 20170172414; 20170172501; 20170172520;20170173262; 20170177023; 20170181693; 20170185149; 20170188865;20170188872; 20170188947; 20170188992; 20170189691; 20170196497;20170202474; 20170202518; 20170203154; 20170209053; and 20170209083.

There are many approaches to time-frequency decomposition of EEG data,including the short-term Fouriertransform (STFT), (Gabor D. Theory ofCommunication. J. Inst Electr. Engrs. 1946; 93:429-457) continuous(Daubechies I. Ten Lectures on Wavelets. Philadelphia, Pa.: Society forIndustial and Applied Mathematics; 1992:357. 21. Combes J M, GrossmannA, Tchamitchian P. Wavelets: Time-Frequency Methods and PhaseSpace-Proceedings of the International Conference; Dec. 14-18, 1987;Marseille, France) or discrete (Mallat S G. A theory for multiresolutionsignal decomposition: the wavelet representation. IEEE Trans PatternAnal Mach Intell. 1989; 11:674-693) wavelet transforms, Hilberttransform (Lyons R G. Understanding Digital Signal Processing. 2nd ed.Upper Saddle River, N.J.: Prentice Hall PTR; 2004:688), and matchingpursuits (Mallat S, Zhang Z. Matching pursuits with time-frequencydictionaries. IEEE Trans. Signal Proc. 1993; 41(12):3397-3415).Prototype analysis systems may be implemented using, for example, MatLabwith the Wavelet Toolbox, www.mathworks.com/products/wavelethtml.

See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 6,196,972; 6,338,713;6,442,421; 6,507,754; 6,524,249; 6,547,736; 6,616,611; 6,816,744;6,865,494; 6,915,241; 6,936,012; 6,996,261; 7,043,293; 7,054,454;7,079,977; 7,128,713; 7,146,211; 7,149,572; 7,164,941; 7,209,788;7,254,439; 7,280,867; 7,282,030; 7,321,837; 7,330,032; 7,333,619;7,381,185; 7,537,568; 7,559,903; 7,565,193; 7,567,693; 7,604,603;7,624,293; 7,640,055; 7,715,919; 7,725,174; 7,729,755; 7,751,878;7,778,693; 7,794,406; 7,797,040; 7,801,592; 7,803,118; 7,803,119;7,879,043; 7,896,807; 7,899,524; 7,917,206; 7,933,646; 7,937,138;7,976,465; 8,014,847; 8,033,996; 8,073,534; 8,095,210; 8,137,269;8,137,270; 8,175,696; 8,177,724; 8,177,726; 8,180,601; 8,187,181;8,197,437; 8,233,965; 8,236,005; 8,244,341; 8,248,069; 8,249,698;8,280,514; 8,295,914; 8,326,433; 8,335,664; 8,346,342; 8,355,768;8,386,312; 8,386,313; 8,392,250; 8,392,253; 8,392,254; 8,392,255;8,396,542; 8,406,841; 8,406,862; 8,412,655; 8,428,703; 8,428,704;8,463,374; 8,464,288; 8,475,387; 8,483,815; 8,494,610; 8,494,829;8,494,905; 8,498,699; 8,509,881; 8,533,042; 8,548,786; 8,571,629;8,579,786; 8,591,419; 8,606,360; 8,628,480; 8,655,428; 8,666,478;8,682,422; 8,706,183; 8,706,205; 8,718,747; 8,725,238; 8,738,136;8,747,382; 8,755,877; 8,761,869; 8,762,202; 8,768,449; 8,781,796;8,790,255; 8,790,272; 8,821,408; 8,825,149; 8,831,731; 8,843,210;8,849,392; 8,849,632; 8,855,773; 8,858,440; 8,862,210; 8,862,581;8,903,479; 8,918,178; 8,934,965; 8,951,190; 8,954,139; 8,955,010;8,958,868; 8,983,628; 8,983,629; 8,989,835; 9,020,789; 9,026,217;9,031,644; 9,050,470; 9,060,671; 9,070,492; 9,072,832; 9,072,905;9,078,584; 9,084,896; 9,095,295; 9,101,276; 9,107,595; 9,116,835;9,125,574; 9,149,719; 9,155,487; 9,192,309; 9,198,621; 9,204,835;9,211,417; 9,215,978; 9,232,910; 9,232,984; 9,238,142; 9,242,067;9,247,911; 9,248,286; 9,254,383; 9,277,871; 9,277,873; 9,282,934;9,289,603; 9,302,110; 9,307,944; 9,308,372; 9,320,450; 9,336,535;9,357,941; 9,375,151; 9,375,171; 9,375,571; 9,403,038; 9,415,219;9,427,581; 9,443,141; 9,451,886; 9,454,646; 9,462,956; 9,462,975;9,468,541; 9,471,978; 9,480,402; 9,492,084; 9,504,410; 9,522,278;9,533,113; 9,545,285; 9,560,984; 9,563,740; 9,615,749; 9,616,166;9,622,672; 9,622,676; 9,622,702; 9,622,703; 9,623,240; 9,636,019;9,649,036; 9,659,229; 9,668,694; 9,681,814; 9,681,820; 9,682,232;9,713,428; 20020035338; 20020091319; 20020095099; 20020103428;20020103429; 20020193670; 20030032889; 20030046018; 20030093129;20030160622; 20030185408; 20030216654; 20040039268; 20040049484;20040092809; 20040133119; 20040133120; 20040133390; 20040138536;20040138580; 20040138711; 20040152958; 20040158119; 20050010091;20050018858; 20050033174; 20050075568; 20050085744; 20050119547;20050148893; 20050148894; 20050148895; 20050154290; 20050167588;20050240087; 20050245796; 20050267343; 20050267344; 20050283053;20050283090; 20060020184; 20060036152; 20060036153; 20060074290;20060078183; 20060135879; 20060153396; 20060155495; 20060161384;20060173364; 20060200013; 20060217816; 20060233390; 20060281980;20070016095; 20070066915; 20070100278; 20070179395; 20070179734;20070191704; 20070209669; 20070225932; 20070255122; 20070255135;20070260151; 20070265508; 20070287896; 20080021345; 20080033508;20080064934; 20080074307; 20080077015; 20080091118; 20080097197;20080119716; 20080177196; 20080221401; 20080221441; 20080243014;20080243017; 20080255949; 20080262367; 20090005667; 20090033333;20090036791; 20090054801; 20090062676; 20090177144; 20090220425;20090221930; 20090270758; 20090281448; 20090287271; 20090287272;20090287273; 20090287467; 20090299169; 20090306534; 20090312646;20090318794; 20090322331; 20100030073; 20100036211; 20100049276;20100068751; 20100069739; 20100094152; 20100099975; 20100106041;20100198090; 20100204604; 20100204748; 20100249638; 20100280372;20100331976; 20110004115; 20110015515; 20110015539; 20110040713;20110066041; 20110066042; 20110074396; 20110077538; 20110092834;20110092839; 20110098583; 20110160543; 20110172725; 20110178441;20110184305; 20110191350; 20110218950; 20110257519; 20110270074;20110282230; 20110288431; 20110295143; 20110301441; 20110313268;20110313487; 20120004518; 20120004561; 20120021394; 20120022343;20120029378; 20120041279; 20120046535; 20120053473; 20120053476;20120053478; 20120053479; 20120083708; 20120108918; 20120108997;20120143038; 20120145152; 20120150545; 20120157804; 20120159656;20120172682; 20120184826; 20120197153; 20120209139; 20120253261;20120265267; 20120271151; 20120271376; 20120289869; 20120310105;20120321759; 20130012804; 20130041235; 20130060125; 20130066392;20130066395; 20130072775; 20130079621; 20130102897; 20130116520;20130123607; 20130127708; 20130131438; 20130131461; 20130165804;20130167360; 20130172716; 20130172772; 20130178733; 20130184597;20130204122; 20130211238; 20130223709; 20130226261; 20130237874;20130238049; 20130238050; 20130245416; 20130245424; 20130245485;20130245486; 20130245711; 20130245712; 20130261490; 20130274562;20130289364; 20130295016; 20130310422; 20130310909; 20130317380;20130338518; 20130338803; 20140039279; 20140057232; 20140058218;20140058528; 20140074179; 20140074180; 20140094710; 20140094720;20140107521; 20140142654; 20140148657; 20140148716; 20140148726;20140180153; 20140180160; 20140187901; 20140228702; 20140243647;20140243714; 20140257128; 20140275807; 20140276130; 20140276187;20140303454; 20140303508; 20140309614; 20140316217; 20140316248;20140324118; 20140330334; 20140330335; 20140330336; 20140330404;20140335489; 20140350634; 20140350864; 20150005646; 20150005660;20150011907; 20150018665; 20150018699; 20150018702; 20150025422;20150038869; 20150073294; 20150073306; 20150073505; 20150080671;20150080695; 20150099962; 20150126821; 20150151142; 20150164431;20150190070; 20150190636; 20150190637; 20150196213; 20150196249;20150213191; 20150216439; 20150245800; 20150248470; 20150248615;20150272652; 20150297106; 20150297893; 20150305686; 20150313498;20150366482; 20150379370; 20160000348; 20160007899; 20160022167;20160022168; 20160022207; 20160027423; 20160029965; 20160038042;20160038043; 20160045128; 20160051812; 20160058304; 20160066838;20160107309; 20160113587; 20160120428; 20160120432; 20160120437;20160120457; 20160128596; 20160128597; 20160135754; 20160143594;20160144175; 20160151628; 20160157742; 20160157828; 20160174863;20160174907; 20160176053; 20160183881; 20160184029; 20160198973;20160206380; 20160213261; 20160213317; 20160220850; 20160228028;20160228702; 20160235324; 20160239966; 20160239968; 20160242645;20160242665; 20160242669; 20160242690; 20160249841; 20160250355;20160256063; 20160256105; 20160262664; 20160278653; 20160278713;20160287117; 20160287162; 20160287169; 20160287869; 20160303402;20160331264; 20160331307; 20160345895; 20160345911; 20160346542;20160361041; 20160361546; 20160367186; 20160367198; 20170031440;20170031441; 20170039706; 20170042444; 20170045601; 20170071521;20170079588; 20170079589; 20170091418; 20170113046; 20170120041;20170128015; 20170135594; 20170135626; 20170136240; 20170165020;20170172446; 20170173326; 20170188870; 20170188905; 20170188916;20170188922; and 20170196519.

Single instruction, multiple data processors, such as graphic processingunits including the nVidia CUDA environment or AMD Fireprohigh-performance computing environment are known, and may be employedfor general purpose computing, finding particular application in datamatrix transformations.

-   See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 5,273,038;    5,503,149; 6,240,308; 6,272,370; 6,298,259; 6,370,414; 6,385,479;    6,490,472; 6,556,695; 6,697,660; 6,801,648; 6,907,280; 6,996,261;    7,092,748; 7,254,500; 7,338,455; 7,346,382; 7,490,085; 7,497,828;    7,539,528; 7,565,193; 7,567,693; 7,577,472; 7,597,665; 7,627,370;    7,680,526; 7,729,755; 7,809,434; 7,840,257; 7,860,548; 7,872,235;    7,899,524; 7,904,134; 7,904,139; 7,907,998; 7,983,740; 7,983,741;    8,000,773; 8,014,847; 8,069,125; 8,233,682; 8,233,965; 8,235,907;    8,248,069; 8,356,004; 8,379,952; 8,406,838; 8,423,125; 8,445,851;    8,553,956; 8,586,932; 8,606,349; 8,615,479; 8,644,910; 8,679,009;    8,696,722; 8,712,512; 8,718,747; 8,761,866; 8,781,557; 8,814,923;    8,821,376; 8,834,546; 8,852,103; 8,870,737; 8,936,630; 8,951,189;    8,951,192; 8,958,882; 8,983,155; 9,005,126; 9,020,586; 9,022,936;    9,028,412; 9,033,884; 9,042,958; 9,078,584; 9,101,279; 9,135,400;    9,144,392; 9,149,255; 9,155,521; 9,167,970; 9,179,854; 9,179,858;    9,198,637; 9,204,835; 9,208,558; 9,211,077; 9,213,076; 9,235,685;    9,242,067; 9,247,924; 9,268,014; 9,268,015; 9,271,651; 9,271,674;    9,275,191; 9,292,920; 9,307,925; 9,322,895; 9,326,742; 9,330,206;    9,368,265; 9,395,425; 9,402,558; 9,414,776; 9,436,989; 9,451,883;    9,451,899; 9,468,541; 9,471,978; 9,480,402; 9,480,425; 9,486,168;    9,592,389; 9,615,789; 9,626,756; 9,672,302; 9,672,617; 9,682,232;    20020033454; 20020035317; 20020037095; 20020042563; 20020058867;    20020103428; 20020103429; 20030018277; 20030093004; 20030128801;    20040082862; 20040092809; 20040096395; 20040116791; 20040116798;    20040122787; 20040122790; 20040166536; 20040215082; 20050007091;    20050020918; 20050033154; 20050079636; 20050119547; 20050154290;    20050222639; 20050240253; 20050283053; 20060036152; 20060036153;    20060052706; 20060058683; 20060074290; 20060078183; 20060084858;    20060149160; 20060161218; 20060241382; 20060241718; 20070191704;    20070239059; 20080001600; 20080009772; 20080033291; 20080039737;    20080042067; 20080097235; 20080097785; 20080128626; 20080154126;    20080221441; 20080228077; 20080228239; 20080230702; 20080230705;    20080249430; 20080262327; 20080275340; 20090012387; 20090018407;    20090022825; 20090024050; 20090062660; 20090078875; 20090118610;    20090156907; 20090156955; 20090157323; 20090157481; 20090157482;    20090157625; 20090157751; 20090157813; 20090163777; 20090164131;    20090164132; 20090171164; 20090172540; 20090179642; 20090209831;    20090221930; 20090246138; 20090299169; 20090304582; 20090306532;    20090306534; 20090312808; 20090312817; 20090318773; 20090318794;    20090322331; 20090326604; 20100021378; 20100036233; 20100041949;    20100042011; 20100049482; 20100069739; 20100069777; 20100082506;    20100113959; 20100249573; 20110015515; 20110015539; 20110028827;    20110077503; 20110118536; 20110125077; 20110125078; 20110129129;    20110160543; 20110161011; 20110172509; 20110172553; 20110178359;    20110190846; 20110218405; 20110224571; 20110230738; 20110257519;    20110263962; 20110263968; 20110270074; 20110288400; 20110301448;    20110306845; 20110306846; 20110313274; 20120021394; 20120022343;    20120035433; 20120053483; 20120163689; 20120165904; 20120215114;    20120219195; 20120219507; 20120245474; 20120253261; 20120253434;    20120289854; 20120310107; 20120316793; 20130012804; 20130060125;    20130063550; 20130085678; 20130096408; 20130110616; 20130116561;    20130123607; 20130131438; 20130131461; 20130178693; 20130178733;    20130184558; 20130211238; 20130221961; 20130245424; 20130274586;    20130289385; 20130289386; 20130303934; 20140058528; 20140066763;    20140119621; 20140151563; 20140155730; 20140163368; 20140171757;    20140180088; 20140180092; 20140180093; 20140180094; 20140180095;    20140180096; 20140180097; 20140180099; 20140180100; 20140180112;    20140180113; 20140180176; 20140180177; 20140184550; 20140193336;    20140200414; 20140243614; 20140257047; 20140275807; 20140303486;    20140315169; 20140316248; 20140323849; 20140335489; 20140343397;    20140343399; 20140343408; 20140364721; 20140378830; 20150011866;    20150038812; 20150051663; 20150099959; 20150112409; 20150119658;    20150119689; 20150148700; 20150150473; 20150196800; 20150200046;    20150219732; 20150223905; 20150227702; 20150247921; 20150248615;    20150253410; 20150289779; 20150290453; 20150290454; 20150313540;    20150317796; 20150324692; 20150366482; 20150375006; 20160005320;    20160027342; 20160029965; 20160051161; 20160051162; 20160055304;    20160058304; 20160058392; 20160066838; 20160103487; 20160120437;    20160120457; 20160143541; 20160157742; 20160184029; 20160196393;    20160228702; 20160231401; 20160239966; 20160239968; 20160260216;    20160267809; 20160270723; 20160302720; 20160303397; 20160317077;    20160345911; 20170027539; 20170039706; 20170045601; 20170061034;    20170085855; 20170091418; 20170112403; 20170113046; 20170120041;    20170160360; 20170164861; 20170169714; 20170172527; and 20170202475.

Statistical analysis may be presented in a form that permitsparallelization, which can be efficiently implemented using variousparallel processors, a common form of which is a SIMD (singleinstruction, multiple data) processor, found in typical graphicsprocessors (GPUs).

-   See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos. 8,406,890;    8,509,879; 8,542,916; 8,852,103; 8,934,986; 9,022,936; 9,028,412;    9,031,653; 9,033,884; 9,037,530; 9,055,974; 9,149,255; 9,155,521;    9,198,637; 9,247,924; 9,268,014; 9,268,015; 9,367,131; 9,4147,80;    9,420,970; 9,430,615; 9,442,525; 9,444,998; 9,445,763; 9,462,956;    9,474,481; 9,489,854; 9,504,420; 9,510,790; 9,519,981; 9,526,906;    9,538,948; 9,585,581; 9,622,672; 9,641,665; 9,652,626; 9,684,335;    9,687,187; 9,693,684; 9,693,724; 9,706,963; 9,712,736; 20090118622;    20100098289; 20110066041; 20110066042; 20110098583; 20110301441;    20120130204; 20120265271; 20120321759; 20130060158; 20130113816;    20130131438; 20130184786; 20140031889; 20140031903; 20140039975;    20140114889; 20140226131; 20140279341; 20140296733; 20140303424;    20140313303; 20140315169; 20140316235; 20140364721; 20140378810;    20150003698; 20150003699; 20150005640; 20150005644; 20150006186;    20150029087; 20150033245; 20150033258; 20150033259; 20150033262;    20150033266; 20150081226; 20150088093; 20150093729; 20150105701;    20150112899; 20150126845; 20150150122; 20150190062; 20150190070;    20150190077; 20150190094; 20150192776; 20150196213; 20150196800;    20150199010; 20150241916; 20150242608; 20150272496; 20150272510;    20150282705; 20150282749; 20150289217; 20150297109; 20150305689;    20150335295; 20150351655; 20150366482; 20160027342; 20160029896;    20160058366; 20160058376; 20160058673; 20160060926; 20160065724;    20160065840; 20160077547; 20160081625; 20160103487; 20160104006;    20160109959; 20160113517; 20160120048; 20160120428; 20160120457;    20160125228; 20160157773; 20160157828; 20160183812; 20160191517;    20160193499; 20160196185; 20160196635; 20160206241; 20160213317;    20160228064; 20160235341; 20160235359; 20160249857; 20160249864;    20160256086; 20160262680; 20160262685; 20160270656; 20160278672;    20160282113; 20160287142; 20160306942; 20160310071; 20160317056;    20160324445; 20160324457; 20160342241; 20160360100; 20160361027;    20160366462; 20160367138; 20160367195; 20160374616; 20160378608;    20160378965; 20170000324; 20170000325; 20170000326; 20170000329;    20170000330; 20170000331; 20170000332; 20170000333; 20170000334;    20170000335; 20170000337; 20170000340; 20170000341; 20170000342;    20170000343; 20170000345; 20170000454; 20170000683; 20170001032;    20170007111; 20170007115; 20170007116; 20170007122; 20170007123;    20170007182; 20170007450; 20170007799; 20170007843; 20170010469;    20170010470; 20170013562; 20170017083; 20170020627; 20170027521;    20170028563; 20170031440; 20170032221; 20170035309; 20170035317;    20170041699; 20170042485; 20170046052; 20170065349; 20170086695;    20170086727; 20170090475; 20170103440; 20170112446; 20170113056;    20170128006; 20170143249; 20170143442; 20170156593; 20170156606;    20170164893; 20170171441; 20170172499; 20170173262; 20170185714;    20170188933; 20170196503; 20170205259; 20170206913; and 20170214786.

Artificial neural networks have been employed to analyze EEG signals.

See, U.S. Patent and Pub. App. Nos. U.S. Pat. No. 9,443,141;20110218950; 20150248167; 20150248764; 20150248765; 20150310862;20150331929; 20150338915; 20160026913; 20160062459; 20160085302;20160125572; 20160247064; 20160274660; 20170053665; 20170069306;20170173262; and 20170206691.

-   Amari, S., Natural gradient works efficiently in learning, Neural    Computation 10:251-276, 1998.-   Amari S., Cichocki, A. & Yang, H. H., A new learning algorithm for    blind signal separation. In: Advances in Neural Information    Processing Systems 8, MIT Press, 1996.-   Bandettini P A, Wong E C, Hinks R S, Tikofsky R S, Hyde J S, lime    course EPI of human brain function during task activation. Magn    Reson Med 25:390-7, 1992.-   Bell A. J. & Sejnowski T. J. An information-maximization approach to    blind separation and blind deconvolution. Neural Comput 7:1129-59,    1995.-   Bell, A. J. & Sejnowski, T. J., Learning the higher-order structure    of a natural sound, Network: Computaion in Neural Systems 7, 1996b.-   Bench C J, Frith C D, Grasby P M, Friston K J, Paulesu E, Frackowiak    R S, Dolan R J, Investigations of the functional anatomy of    attention using the Stroop test Neuropsychologia 31:907-22, 1993.-   Boynton G M, Engel S A, Glover G H, Heeger D J, Linear systems    analysis of functional magnetic resonance imaging in human V1. J    Neurosci 16:4207-21, 1996.-   Bringer, Julien, Hervé Chabanne, and Bruno Kindarji. “Error-tolerant    searchable encryption.” In Communications, 2009. ICC'09. IEEE    International Conference on, pp. 1-6. IEEE, 2009.-   Buckner, R. L., Bandettini, P. A., O'Craven, K M, Savoy, R. L.,    Petersen, S. E., Raichle, M. E. & Rosen, B. R., Proc Natl Acad Sci    USA 93, 14878-83, 1996.-   Cardoso, J-F. & Laheld, B., Equivalent adaptive source separation,    IEEE Trans. Signal Proc., in press.-   Chapman, R. M. & McCrary, J. W., EP component identification and    measurement by principal components analysis. Brain Lang. 27,    288-301, 1995.-   Cichocki A., Unbehauen R., & Rummert E., Robust learning algorithm    for blind separation of signals, Electronics Letters 30, 1386-1387,    1994.-   Comon P, Independent component analysis, A new concept? Signal    Processing 36:11-20, 1994.-   Cover, T. M. & Thomas, J. A., Elements of Information Theory John    Wiley, 1991.-   Cox, R. W., AFNI: software for analysis and visualization of    functional magnetic resonance neuroimages. Comput Biomed Res    29:162-73, 1996.-   Cox, R. W. & Hyde J. S. Software tools for analysis and    visualization of fMRI data, NMR in Biomedicine, in press.-   Dale, A. M. & Sereno, M. I., Improved localization of cortical    activity by combining EEG and MEG with MRI cortical surface    reconstruction—a linear approach. J. Cogn. Neurosci. 5:162-176,    1993.-   Friston K. J., Modes or models: A critique on independent component    analysis for fMRI. Trends in Cognitive Sciences, in press.-   Friston K. J., Commentary and opinion: II. Statistical parametic    mapping: ontology and current issues. J Cereb Blood Flow Metab    15:361-70, 1995.-   Friston K. J., Statistical Parametric Mapping and Other Analyses of    Functional Imaging Data. In: A. W. Toga, J. C. Mazziotta eds., Brain    Mapping, The Methods. San Diego: Academic Press, 1996:363-396, 1995.-   Friston K J, Frith C D, Liddle P F, Frackowiak R S, Functional    connectivity: the principal-component analysis of large (PET) data    sets. J Cereb Blood Flow Metab 13:5-14, 1993.-   Friston K J, Holmes A P, Worsley K J, Poline J P, Frith C D, and    Frackowiak R. S. J., Statistical Parametric Maps in Functional    Imaging: A General Linear Approach, Human Brain Mapping 2:189-210,    1995.-   Friston K J, Williams S, Howard R, Frackowiak R S and Tumer R,    Movement-related effects in fMRI time-series. Magn Reson Med    35:346-55, 1996.-   Galambos, R. and S. Makeig, “Dynamic changes in steady-state    potentials,” in: Dynamics of Sensory and Cognitive Processing of the    Brain, ed. E. Basar Springer, pp. 178-199, 1987.-   Galambos, R., S. Makeig, and P. Talmachoff, A 40 Hz auditory    potential recorded from the human scalp, Proc Natl Acad Sci USA    78(4):2643-2647, 1981.-   Galil, Zvi, Stuart Haber, and Moti Yung. “Cryptographic computation:    Secure fault-tolerant protocols and the public-key model.” In    Conference on the Theory and Application of Cryptographic    Techniques, pp. 135-155. Springer, Berlin, Heidelberg, 1987.-   George J S, Aine C J, Mosher J C, Schmidt D M, Ranken D M, Schlitt H    A, Wood C C, Lewine J D, Sanders J A, Belliveau J W. Mapping    function in the human brain with magnetoencephalography, anatomical    magnetic resonance imaging, and functional magnetic resonance    imaging. J Clin Neurophysiol 12:406-31, 1995.-   Ives, J. R., Warach S, Schmitt F, Edelman R R and Schomer D L.    Monitoring the patients EEG during echo planar MRI,    Electroencephalogr Clin Neurophysiol, 87: 417-420, 1993.-   Jackson, J. E., A User's Guide to Principal Components. New York:    John Wiley & Sons, Inc., 1991.-   Jokeit, H. and Makeig, S., Different event-related patterns of    gamma-band power in brain waves of fast- and slow-reacting subjects,    Proc. Nat Acad. Sci USA 91:6339-6343, 1994.-   Juels, Ari, and Madhu Sudan. “A fuzzy vault scheme.” Designs, Codes    and Cryptography 38, no. 2 (2006): 237-257.-   Jueptner, M., K. M. Stephan, C. D. Frith, D. J. Brooks, R. S J.    Frackowiak & R. E. Passingham, Anatomy of Motor Learning. I. Frontal    Cortex and Attention. J. Neurophysiology 77:1313-1324, 1977.-   Jung, T-P., Humphries, C., Lee, T-W., Makeig, S., McKeown, M.,    Iragui, V. and Sejnowski, T. J., “Extended ICA removes artifacts    from electroencephalographic recordings,” In: Advances in Neural    Information Processing Systems 10: MIT Press, Cambridge, Mass., in    press.-   Jung, T-P., Humphries, C., Lee, T-W., McKeown, M. J., Iragui, V.,    Makeig, S. & Sejnowski, T. J., Removing electroencephalographic    artifacts by blind source separation, submitted-a.-   Jung, T-P., S. Makeig, M. Stensmo & T. Sejnowski, Estimating    Alertness from the EEG Power Spectrum, IEEE Transactions on    Biomedical Engineering, 44(1), 60-69, 1997.-   Jung, T-P., Makeig, S., Westerfield, M., Townsend, J.,    Courchesne, E. and Sejnowski, T. J., Analysis and visualization of    single-trial event-related potentials, submitted-b.-   Jutten, C. & Herault, J., Blind separation of sources, part I: an    adaptive algorithm based on neuromimetic architecture. Signal    Processing 24, 1-10, 1991.-   Karhumen, J., Oja, E., Wang, L., Vigario, R. & Joutsenalo, J., A    class of neural networks for independent component analysis, IEEE    Trans. Neural Networks, in press.-   Kwong K. K., Functional magnetic resonance imaging with echo planar    imaging. Magn Reson Q 11:1-20, 1995.-   Kwong K. K., Belliveau J W, Chesler D A, Goldberg I E, Weisskoff R    M, Poncelet B P, Kennedy D N, Hoppel B E, Cohen M S, Tumer R, et    al., Dynamic magnetic resonance imaging of human brain activity    during primary sensory stimulation. Proc Nati Acad Sci USA    89:5675-9, 1992.-   Lee, T.-W., Girolami, M., and Sejnowski, T. J., Independent    component analysis using an extended infomax algorithm for mixed    Sub-gaussian and Super-gaussian sources, Neural Computation,    submitted for publication.-   Lewicki, Michael S., and Sejnowski, Terence J., Learning nonlinear    overcomplete representations for efficient coding, Eds. M. Keams, M.    Jordan, and S. Solla, Advances in Neural Information Processing    Systems 10, in press.-   Linsker, R., Local synaptic learning rules suffice to maximise    mutual information in a linear network. Neural Computation 4,    691-702, 1992.-   Liu A K, Belliveau J W, Dale A M. Spatiotemporal imaging of human    brain activity using functional MRI-constrained    magnetoencephalography data: Monte Carlo simulations. Proc Natl Acad    Sci USA 95:8945-50, 1998-   Manoach D S, Schlaug G, Siewert B, Darby D G, Bly B M, Benfield A,    Edelman R R, Warach S, Prefrontal cortex fMRI signal changes are    correlated with working memory load. Neuroreport 8:545-9, 1997.-   McCarthy, G., Luby, M., Gore, J. and Goldman-Rakic, P., Infrequent    events transiently activate human prefrontal and parietal cortex as    measured by functional MRI. J. Neurophysiology 77: 1630-1634, 1997.-   McKeown, M., Makeig, S., Brown, G., Jung, T-P., Kindermann, S.,    Bell, Iragui, V. and Sejnowski, T. J., Blind separation of    functional magnetic resonance imaging (fMRI) data, Human Brain    Mapping, 6:160,18, 1998a.-   McKeown, M. J., Humphries, C., Achermann, P., Borbely, A. A. and    Sejnowski, T. J., A new method for detecting state changes in the    EEG: exploratory application to sleep data. J. Sleep Res. 7 suppl.    1: 48-56, 1998b.-   McKeown, M. J., Tzyy-Ping Jung, Scott Makeig, Greg Brown, Sandra S.    Kindermann, Te-Won Lee and Terrence J. Sejnowski, Spatially    independent activity patterns in functional magnetic resonance    imaging data during the Stroop color-naming task, Proc. Nat. Acad.    Sci USA, 95:803-810, 1998c.-   McKeown, M. J. and Sejnowski, T. J., Independent component analysis    of fMRI data: examining the assumptions. Human Brain Mapping    6:368-372, 1998d.-   Makeig, S. Auditory event-related dynamics of the EEG spectrum and    effects of exposure to tones, Electroencephalogr Clin Neurophysiol,    86:283-293, 1993.-   Makeig, S. Toolbox for independent component analysis of    psychophysiological data, (World Wide Web publication)    www.cnl.salk.edu/-scottfica.html, 1997.-   Makeig, S. and Galambos, R., The CERP: Event-related perturbations    in steady-state responses, in: Brain Dynamics Progress and    Perspectives, (pp. 375-400), ed. E. Basar and T. H. Bullock, 1989.-   Makeig, S. and Inlow, M., Lapses in alertness: coherence of    fluctuations in performance and the EEG spectrum, Electroencephalogr    clin Neurophysiol, 86:23-35, 1993.-   Makeig, S. and Jung, T-P., Changes in alertness are a principal    component of variance in the EEG spectrum, NeuroReport 7:213-216,    1995.-   Makeig, S. and T-P. Jung, Tonic, phasic, and transient EEG    correlates of auditory awareness during drowsiness, Cognitive Brain    Research 4:15-25, 1996.-   Makeig, S., Bell, A. J., Jung, T-P. and Sejnowski, T. J.,    “Independent component analysis of electroencephalographic data,”    In: D. Touretzky, M. Mozer and M. Hasselmo (Eds). Advances in Neural    Information Processing Systems 8:145-151 MIT Press, Cambridge,    Mass., 1996.-   Makeig, S., Jung, T-P, and Sejnowski, T. J., “Using feedforward    neural networks to monitor alertness from changes in EEG correlation    and coherence,” In: D. Touretzky, M. Mozer & M. Hasselmo(Eds).    Advances in Neural Information Processing Systems 8:931-937 MIT    Press, Cambridge, Mass., 1996.-   Makeig, S., T-P. Jung, D. Ghahremani, A. J. Bell & T. J. Sejnowski,    Blind separation of auditory event-related brain responses into    independent components. Proc. Natl. Acad. Sci. USA, 94:10979-10984,    1997.-   Makeig, S., Westerfield, M., Jung, T-P., Covington, J., Townsend,    J., Sejnowski, T. J. and Courchesne, E., Independent components of    the late positive event-related potential in a visual spatial    attention task, submitted.-   Mira P P, Ogawa S, Hu X, Ugurbil K, The nature of spatiotemporal    changes in cerebral hemodynamics as manifested in functional    magnetic resonance imaging. Magn Reson Med.37:511-8, 1997.-   Nobre A C, Sebestyen G N, Gitelman D R, Mesulam M M, Frackowiak R S,    Frith C D, Functional localization of the system for visuospatial    attention using positron emission tomography. Brain 120:515-33,    1997.-   Nunez, P. L., Electric Fields of the Brain. New York: Oxford, 1981.-   Ogawa S, Tank D W, Menon R, Ellermann J M, Kim S G, Merkle H,    Ugurbil K, Intrinsic signal changes accompanying sensory    stimulation: functional brain mapping with magnetic resonance    imaging. Proc Natl Acad Sci USA 89:5951-5, 1992.-   Pearlmutter, B. and Parra, L.C. Maximum likelihood blind source    separation: a context-sensitive generalization of ICA. In: M. C.    Mozer, M. I. Jordan and T. Petsche (Eds.), Advances in Neural    Information Processing Systems 9:613-619 MIT Press, Cambridge,    Mass., 1996.-   Sakai K, Hikosaka O, Miyauchi S, Takino R, Sasaki Y, Putz B.    Transition of brain activation from frontal to parietal areas in    visuomotor sequence learning. J Neurosci 18:1827-40, 1998.-   Sahai, Amit, and Brent Waters. “Fuzzy identity-based encryption.” In    Annual International Conference on the Theory and Applications of    Cryptographic Techniques, pp. 457-473. Springer, Berlin, Heidelberg,    2005.-   Scherg, M. & Von Cramon, D., Evoked dipole source potentials of the    human auditory cortex. Electroencephalogr. Clin. Neurophysiol.    65:344-601, 1986.-   Tallon-Baudry, C., Bertrand, O., Delpuech, C., & Pemier, J.,    Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz    Visual Responses in Human. J. Neurosci. 16: 4240-4249, 1996.-   Thaker, Darshan D., Diana Franklin, John Oliver, Susmit Biswas,    Derek Lockhart, Tzvetan Metodi, and Frederic T. Chong.    “Characterization of error-tolerant applications when protecting    control data.” In Workload Characterization, 2006 IEEE International    Symposium on, pp. 142-149. IEEE, 2006.-   Tulving E, Markowitsch H J, Craik F E, Habib R, Houle S, Novelty and    familiarity activations in PET studies of memory encoding and    retrieval. Cereb Cortex 6:71-9, 1996.-   Warach, S., J. R. Ives, G. Schaug, M. R. Patel, D. G. Darby, V.    Thangaraj, R. R. Edelman and D. L. Schomer, EEG-triggered    echo-planar functional MRI in epilepsy, Neurology 47: 89-93, 1996.

Principal Component Analysis

Principal component analysis (PCA) is a statistical procedure that usesan orthogonal transformation to convert a set of observations ofpossibly correlated variables into a set of values of linearlyuncorrelated variables called principal components. If there are nobservations with p variables, then the number of distinct principalcomponents is min(n−1,p). This transformation is defined in such a waythat the first principal component has the largest possible variance(that is, accounts for as much of the variability in the data aspossible), and each succeeding component in turn has the highestvariance possible under the constraint that it is orthogonal to thepreceding components. The resulting vectors are an uncorrelatedorthogonal basis set PCA is sensitive to the relative scaling of theoriginal variables. PCA is the simplest of the true eigenvector-basedmultivariate analyses. Often, its operation can be thought of asrevealing the internal structure of the data in a way that best explainsthe variance in the data. If a multivariate dataset is visualized as aset of coordinates in a high-dimensional data space (1 axis pervariable), PCA can supply the user with a lower-dimensional picture, aprojection of this object when viewed from its most informativeviewpoint. This is done by using only the first few principal componentsso that the dimensionality of the transformed data is reduced. PCA isclosely related to factor analysis. Factor analysis typicallyincorporates more domain specific assumptions about the underlyingstructure and solves eigenvectors of a slightly different matrix. PCA isalso related to canonical correlation analysis (CCA). CCA definescoordinate systems that optimally describe the cross-covariance betweentwo datasets while PCA defines a new orthogonal coordinate system thatoptimally describes variance in a single dataset See,en.wikipedia.org/wiki/Principal_component_analysis.

A general model for confirmatory factor analysis is expressed asx=α+Λξ+ε. The covariance matrix is expressed as E[(x−μ)(x−μ)′]=ΛΦΛ′+Θ.If residual covariance matrix Θ=0 and correlation matrix among latentfactors Φ=I, then factor analysis is equivalent to principal componentanalysis and the resulting covariance matrix is simplified to Σ=ΛΛ′.When there are p number of variables and all p components (or factors)are extracted, this covariance matrix can alternatively be expressedinto Σ=DΛD′, or Σ=λDAD′, where D=n×p orthogonal matrix of eigenvectors,and Λ=λΛ, p×p matrix of eigenvalues, where λ is a scalar and A is adiagonal matrix whose elements are proportional to the eigenvalues of Σ.The following three components determine the geometric features of theobserved data: λ parameterizes the volume of the observation, Dindicates the orientation, and A represents the shape of theobservation.

When population heterogeneity is explicitly hypothesized as inmodel-based cluster analysis, the observed covariance matrix isdecomposed into the following general form Σ_(k)=λ_(k) D_(k) A_(k) D_(k)^(T),

where λ_(k) parameterizes the volume of the k^(th) cluster, D_(k)indicates the orientation of that cluster, and A_(k) represents theshape of that cluster. The subscript k indicates that each component (orcluster) can have different volume, shape, and orientation.

Assume a random vector X, taking values in

^(m), has a mean and covariance matrix of μ_(X) and Σ_(X), respectively.λ₁>λ2> . . . >λ_(m)>0 are ordered eigenvalues of Σ_(X), such that thei-th eigenvalue of Σ_(X) means the i-th largest of them. Similarly, avector α_(i) is the i-th eigenvector of Σ_(X) when it corresponds to thei-th eigenvalue of Σ_(X). To derive the form of principal components(PCs), consider the optimization problem of maximizing var[α₁ ^(T)X]=α₁^(T)Σ_(X)α₁, subject to α₁ ^(T)α₁=1. The Lagrange multiplier method isused to solve this question.

  L(α₁, φ₁) = α₁^(T)∑_(X)α₁ + φ₁(α₁^(T)α₁ − 1)$\frac{\partial L}{\partial\alpha_{1}} = {{{2{\sum_{X}\alpha_{1}}} + {2\varphi_{1}\alpha_{1}}} = {\left. 0\Rightarrow{\sum_{X}\alpha_{1}} \right. = {\left. {{- \varphi_{1}}\alpha_{1}}\Rightarrow{{var}\left\lbrack {\alpha_{1}^{T}X} \right\rbrack} \right. = {{{- \varphi_{1}}\alpha_{1}^{T}\alpha_{1}} = {- {\varphi_{1}.}}}}}}$

Because −ϕ₁ is the eigenvalue of Σ_(X), with α₁ being the correspondingnormalized eigenvector, var[α₁ ^(T)X] is maximized by choosing α₁ to bethe first eigenvector of Σ_(X). In this case, z₁=α₁ ^(T)X is named thefirst PC of X, α₁ is the vector of coefficients for z₁, and var(z₁)=λ₁.

To find the second PC, z₂=α₂X, we need to maximize var[α₂ ^(T)X]=α₂^(T)Σ_(X)α₂ subject to z₂ being uncorrelated with z₁. Because cov(α₁^(T) X,α₂ ^(T)X)=0⇒α₁ ^(T)Σ_(X)α₂=0⇒α₁ ^(T)α₂ ^(T)=0, this problem isequivalently set as maximizing α₂ ^(T)Σ_(X)α₂, subject to α₁ ^(T)α₂=0,and α₂ ^(T)α₂=1. We still make use of the Lagrange multiplier method.

  L(α₂, φ₁, φ₂) = α₂^(T)∑_(X)α₂ + φ₁α₁^(T)α₂ + φ₂(α₂^(T)α₂ − 1)$\frac{\partial L}{\partial\alpha_{2}} = {{{2{\sum_{X}\alpha_{2}}} + {\varphi_{1}\alpha_{1}} + {2\varphi_{2}\alpha_{2}}} = {\left. 0\Rightarrow{\alpha_{1}^{T}\left( {{2{\sum_{X}\alpha_{2}}} + {\varphi_{1}\alpha_{1}} + {2\varphi_{2}\alpha_{2}}} \right)} \right. = {\left. 0\Rightarrow\varphi_{1} \right. = {\left. 0\Rightarrow{\sum_{X}\alpha_{2}} \right. = {\left. {{- \varphi_{2}}\alpha_{2}}\Rightarrow{\alpha_{2}^{T}{\sum_{X}\alpha_{2}}} \right. = {- {\varphi_{2}.}}}}}}}$

Because −ϕ₂ is the eigenvalue of Σ_(X), with α₂ being the correspondingnormalized eigenvector, var[α₂ ^(T)X] is maximized by choosing α₂ to bethe second eigenvector of Σ_(X). In this case, z₂=α₂ ^(T)X is named thesecond PC of X, α₂ is the vector of coefficients for z₂, and var(z₂)=λ₂.Continuing in this way, it can be shown that the i-th PC z_(i)=α_(i)^(T)X is constructed by selecting α_(i) to be the i-th eigenvector ofΣ_(X), and has variance of λ_(i). The key result in regards to PCA isthat the principal components are the only set of linear functions oforiginal data that are uncorrelated and have orthogonal vectors ofcoefficients.

For any positive integer P≤m, let B=[β₁, β₂, . . . , β_(p)] be an realm×p matrix with orthonormal columns, i.e., β_(i) ^(T) β_(j)=δ_(ij), andY=B^(T) X. Then the trace of covariance matrix of Y is maximized bytaking B=[α₁, α₂, . . . , α_(p)], where α_(i) is the i-th eigenvector ofΣ_(X). Because Σ_(X) is symmetric with all distinct eigenvalues, so {α₁,α₂ . . . , α_(m)} is an orthonormal basis with α_(i) being the i-theigenvector of Σ_(X), and we can represent the columns of B as

${\beta_{i} = {\sum\limits_{j = 1}^{m}{c_{ji}\alpha_{j}}}},$

i=1, . . . , p, So we have B=PC, where P=[α₁, . . . α_(m)], C={c_(ij)}is an m×p matrix. Then, P^(T)Σ_(X)P=Λ, with Λ being a diagonal matrixwhose k-th diagonal element is λ_(k), and the covariance matrix of Y is,

Σ_(Y) =B ^(T)Σ_(X) B=C ^(T) P ^(T)Σ_(X) PC=C ^(T) ΛC=λ ₁ c ₁ c ₁ ^(T)+ .. . +λ_(m) c _(m) c _(m) ^(T)

where c_(i) ^(T) is the i-th row of C. So,

${{trace}\left( \sum_{Y} \right)} = {{\sum\limits_{i = 1}^{m}{\lambda_{i}{{trace}\left( {c_{i}c_{i}^{T}} \right)}}} = {{\sum\limits_{i = 1}^{m}{\lambda_{i}{{trace}\left( {c_{i}^{T}c_{i}} \right)}}} = {{\sum\limits_{i = 1}^{m}{\lambda_{i}c_{i}^{T}c_{i}}} = {\sum\limits_{i = 1}^{m}{\left( {\sum\limits_{j = 1}^{p}c_{ij}^{2}} \right){\lambda_{i}.}}}}}}$

Because C^(T)C=B^(T)PP^(T)B=B^(T)B=I, so

${{{trace}\left( {C^{T}C} \right)} = {{\sum\limits_{i = 1}^{m}{\sum\limits_{j = 1}^{p}c_{ij}^{2}}} = p}},$

and the columns of C are orthonormal. By the Gram-Schmidt method, C canexpand to D, such that D has its columns as an orthonormal basis of

^(m) and contains C as its first p columns. D is square shape, thusbeing an orthogonal matrix and having its rows as another orthonormalbasis of

^(m). One row of C is a part of one row of D, so

${{\sum\limits_{j = 1}^{p}c_{ij}^{2}} \leq 1},$

i=1 i=, . . . , m. Considering the constraints

${{\sum\limits_{j = 1}^{p}c_{ij}^{2}} \leq 1},{{\sum\limits_{i = 1}^{m}{\sum\limits_{j = 1}^{p}c_{ij}^{2}}} = p}$

and the objective

$\sum\limits_{i = 1}^{m}{\left( {\sum\limits_{j = 1}^{p}c_{ij}^{2}} \right){\lambda_{i}.}}$

We derive that trace(Σ_(Y)) is maximized if

${\sum\limits_{j = 1}^{p}c_{ij}^{2}} = 1$

for i=1, . . . , , p, and

${\sum\limits_{j = 1}^{p}c_{ij}^{2}} = 0$

for i=p+1, . . . , m. When B=[α₁,α₂, . . . , α_(p)], straightforwardcalculation yields that C is an all-zero matrix except c_(ii)=1, i=1, .. . , p. This fulfills the maximization condition. Actually, by takingB=[γ₁, γ₂, . . . , γ_(p)], where {γ₁, γ₂, . . . , γ_(p)} is anyorthonormal basis of the subspace of span{α₁, α₂, . . . , α_(p)}, themaximization condition is also satisfied, yielding the same trace ofcovariance matrix of Y.

Suppose that we wish to approximate the random vector X by itsprojection onto a subspace spanned by columns of B, where B=[β₁, β₂, . .. , β_(p)] is a real m×p matrix with orthonormal columns, i.e., β_(i)^(T)β_(j)=δ_(ij). If σ_(i) ² is the residual variance for each componentof X, then

$\sum\limits_{i = 1}^{m}\sigma_{i}^{2}$

is minimized if B=[α₁, α₂, . . . , α_(p)], where {α₁, α₂, . . . , α_(p)}are the first p eigenvectors of Σ_(X). In other words, the trace ofcovariance matrix of X−BB^(T)X is minimized if B=[α₁, α₂, . . . ,α_(p)]. When E(X)=0, which is a commonly applied preprocessing step indata analysis methods, this property is saying that E∥X−BB^(T)X∥² isminimized if B=[α₁, α₂, . . . , α_(p)].

The projection of a random vector X onto a subspace spanned by columnsof B is {circumflex over (X)}=BB^(T)X. Then the residual vector isε=X−BB^(T)X, which has a covariance matrix

Σ_(ε)=(I−BB ^(T))Σ_(X)(I−BB ^(T)). Then,

${\sum\limits_{i = 1}^{m}\sigma_{i}^{2}} = {{{trace}\left( \sum_{ɛ} \right)} = {{{trace}\left( {{\sum_{X}{- {\sum_{X}{BB}^{T}}}} - {{BB}^{T}{\sum_{X}{{+ {BB}^{T}}{\sum_{X}{BB}^{T}}}}}} \right)}.}}$

Also, we know:

trace(Σ_(X) BB ^(T))=trace(BB ^(T)Σ_(X))=trace(B ^(T)Σ_(X) B)

trace(BB ^(T)Σ_(X) BB ^(T))=trace(B ^(T)Σ_(X) BB ^(T) B)=trace(B^(T)Σ_(X) B).

The last equation comes from the fact that B has orthonormal columns.So,

${\sum\limits_{i = 1}^{m}\sigma_{i}^{2}} = {{{trace}\left( \sum_{X} \right)} - {{{trace}\left( {B^{T}{\sum_{X}B}} \right)}.}}$

To minimize

${\sum\limits_{i = 1}^{m}\sigma_{i}^{2}},$

it suffices to maximize trace(B^(T)Σ_(X)B). This can be done by choosingB=[α₁, α₂, . . . , α_(p)], where {α₁, α₂, . . . , α_(p)} are the first peigenvectors of Σ_(X), as above.

See, Pietro Amenta, Luigi D'Ambra, “Generalized Constrained PrincipalComponent Analysis with External Information,” (2000). We assume thatdata on K sets of explanatory variables and S criterion variables of nstatistical units are collected in matrices X_(k) (k=1, . . . , K) andY_(s) (s=1, . . . , S) of orders (n×p₁), . . . (n×p_(K) ) and (n×q₁), .. . , (n×q_(s)), respectively. We suppose, without loss of generality,identity matrices for the metrics of the spaces of variables of X_(k)and Y_(s) with D_(n)=diag (1/n), weight matrix of statistical units. Weassume, moreover, that X_(k)'s and Y_(s)'s are centered as to theweights D_(n).

Let x=[X₁| . . . |X_(K)] and Y=[Y₁| . . . |Y_(S)], respectively, be Kand S matrices column linked of orders (n×Σ_(k) p_(k)) and(n×Σ_(s)q_(s)). Let be, also, W_(Y)=YY′ while we denote v_(k) thecoefficients vector (p_(k),1) Of the linear combination for each X_(k)such that z_(k)=X_(k)v_(k). Let C_(k) be the matrix of dimension P_(k)×m(m≤p_(k)) associated to the external information explanatory variablesof set k.

Generalized CPCA (GCPCA) (Amenta, D'Ambra, 1999) with externalinformation consists in seeking for K coefficients vectors v_(k) (or, insame way, K linear combinations z_(k)) subject to the restrictionC′_(k)v_(k)=0 simultaneously, such that

$\begin{matrix}\left\{ \begin{matrix}{\max {\sum\limits_{i = 1}^{K}{\sum\limits_{j = 1}^{K}{\langle{{Y^{\prime}X_{i}v_{i}},{Y^{\prime}X_{j}v_{j}}}\rangle}}}} \\{{with}\mspace{14mu} {the}\mspace{14mu} {constraints}\mspace{14mu} \begin{matrix}{{\sum\limits_{k = 1}^{K}{{X_{k}v_{k}}}^{2}} = 1} \\{{\sum\limits_{k = 1}^{K}{C_{k}^{\prime}v_{k}}} = 0}\end{matrix}}\end{matrix} \right. & (1)\end{matrix}$

or, in equivalent way,

$\left\{ {\begin{matrix}{\max \; {v^{\prime}\left( {A^{\prime}A} \right)}v} \\{{{with}\mspace{14mu} {the}\mspace{14mu} {constraints}\mspace{14mu} \begin{matrix}{{v^{\prime}{Bv}} = 1} \\{{C^{\prime}v} = 0}\end{matrix}\mspace{14mu} {or}}\mspace{14mu}}\end{matrix}\mspace{11mu} \left\{ \begin{matrix}{\max \; f^{\prime}B^{- 0.5}A^{\prime}{AB}^{- 0.5}f} \\{{with}\mspace{14mu} {the}\mspace{14mu} {constraints}\mspace{14mu} \begin{matrix}{{f^{\prime}f} = 1} \\{{C^{\prime}v} = 0}\end{matrix}}\end{matrix} \right.} \right.$

where A=Y′X, B=diag (X′₁X₁, . . . , X′_(K)X_(K)), C′=[C′₁| . . .|C′_(k)], v′=(v′₁| . . . |v_(k)′) and f=B^(0.5) v, with

${A^{\prime}A} = {\begin{bmatrix}{X_{1}^{\prime}{YY}^{\prime}X_{1}} & \ldots & {X_{1}^{\prime}{YY}^{\prime}X_{K}} \\\vdots & \ddots & \vdots \\{X_{K}^{\prime}{YY}^{\prime}X_{1}} & \ldots & {X_{k}^{\prime}{YY}^{\prime}X_{k}}\end{bmatrix}.}$

The constrained maximum problem turns out to be an extension ofcriterion sup Σ_(k)∥z_(k)∥²=1Σ_(i)Σ_(k)

z_(i),z_(k)

(Sabatier, 1993) with more sets of criterion variables with externalinformation. The solution of this constrained maximum problem leads tosolve the eigen-equation

(P _(X) −P _(XB) ₋₁ _(C))W _(Y) g=λg

where g=Xv, P_(X)−P_(XB) ₋₁ _(C)=Σ_(k=1) ^(K)(P_(X) _(k) −P_(X) _(k)_((X′) _(k) _(X) _(k) ₎ ₋₁ _(C) _(k) ) is the oblique projector operatorassociated to the direct sum decomposition of

^(n)

^(n)=Im(P _(X) −P _(XB) ₋₁ _(C)){dot over (⊕)}Im(P _(C)){dot over(⊕)}Ker(P _(X))

with P_(X) _(k) =X_(k) (X′_(k)X_(k))⁻¹X′_(k) andP_(C)=C(C′B⁻¹C)⁻¹C′B⁻¹,respectively, I and B⁻¹ orthogonal projectoroperators onto the subspaces spanned by the columns of matrices X_(k)and C. Furthermore, P_(XB) ₋₁ _(C)=XB⁻¹C(C′B⁻¹C)⁻¹C′B⁻¹X′ is theorthogonal projector operator onto the subspace spanned the columns ofthe matrix XB⁻¹C. Starting from the relation

(P _(X) _(k) −P _(X) _(k) _((X′) _(k) _(X) _(k) ₎ ₋₁ _(C) _(k) )W _(Y)g=λX _(k) v _(k)

(which is obtained from the expression (I−P_(C))X′W_(Y)g=λBv) thecoeffiients vectors v_(k) and the linear combinations z_(k)=X_(k)v_(k)maximizing (1) can be given by the relations

$v_{k} = {\frac{1}{\lambda}\left( {X_{k}^{\prime}X_{k}} \right)^{- 1}\left( {I - P_{C_{k}}} \right)X_{k}^{\prime}W_{Y}{Xv}\mspace{14mu} {and}}$${z_{k} = {\frac{1}{\lambda}\left( {P_{X_{k}} - P_{{X_{k}{({X_{k}^{\prime}X_{k}})}}^{- 1}C_{k}}} \right)W_{Y}{Xv}}},$

respectively.

The solution eigenvector g can be written, as sum of the linearcombinations z_(k): g=Σ_(k)X_(k)v_(k). Notice that the eigenvaluesassociated to the eigen-system are, according to the Sturm theorem,lower or equal than those of GCPCA eigen-system: Σ_(k=1) ^(K)P_(X) _(k)W_(Y)g=λg. See:

-   Amenta P., D'Ambra L. (1994) Analisi non Simmetica delle    Conispondenze Multiple con Vincoli Lineari. Atti S.I.S. XXXVII    Sanremo, April 1994.-   Amenta P., D'Ambra L. (1996) L'Analisi in Componenti Principali in    rapporto ad un sottospazio di riferimento con informazioni esteme,    Quademi del D.M.Q.T.E., Universita di Pescara, n. 18.-   Amenta P., D'Ambra L. (1999) Generalized Constrained Principal    Component Analysis. Atti Riunione Scientifica del Gruppo di    Classficazione dell'IFCS su “Classificazione e Analisi dei Dati”,    Roma.-   D'Ambra L., Lauro N. C. (1982) Analisi in componenti principali in    rapporto ad un sottospazio di riferimento, Rivista di Statistica    Applicata, n.1, vol. 15.-   D'Ambra L., Sabatier R., Amenta P. (1998) Analisi fattoriale delle    matici a Ire vie: sintesi e nuovi approcci, (invited lecture) Atti    XXXIX Riunione SIS.-   Huon de Kermadec F., Durand J. F., Sabatier R. (1996) Comparaison de    methodes de regression pour I'étude des liens entre données    hédoniques, in Third Sensometics Meeting, E.N.T.I.A.A., Nantes.-   Huon de Kermadec F., Durand J. F., Sabatier R. (1997) Comparison    between linear and nonlinear PLS methods to explain overall liking    from sensory characteristics, Food Quality and Preference, 8, n.    5/6.-   Kiers H. A. L. (1991) Hierarchical relations among three way methods    Psychometika, 56.-   Kvalheim O. M. (1988) A partial least squares approach to    interpretative analysis of multivariate analysis, Chemometrics and    Intelligent Laboratory System, 3.-   MacFie H. J. H, Thomson D. M. H. (1988) Preference mapping and    multidimensional scaling methods, in: Sensory Analysis of Foods.    Elsevier Applied Science, London.-   Sabatier R. (1993) Critéres et contraintes pour I'ordination    simultanée de K tableaux, Biométrie et Environement, Masson, 332.-   Schlich P. (1995) Preference mapping: relating consumer preferences    to sensory or instrumental measurements, in: Bioflavour, INRA,    Dijon.-   Wold S., Geladi P., Esbensen K., Ohman J. (1987) Multi-way principal    components and PLS-analysis, J. of Chemometrics, vol. 1.

Spatial Principal Component Analysis (Spatial PCA)

Let J (t, i; α, s) be the current density in voxel i, as estimated byLORETA, in condition α at t time-frames after stimulus onset for subjects. Let area:Voxel→fBA be a function, which assigns to each voxel i∈Voxelthe corresponding fBA b∈fBA. In a first pre-processing step, for eachsubject s, the value of the current density averaged over each fBA iscalculated:

$\begin{matrix}{{x\left( {t,{b;\alpha},s} \right)} = {\frac{1}{N_{b}}{\sum\limits_{i \in b}{J\left( {t,{i;\alpha},s} \right)}}}} & (4)\end{matrix}$

where N_(b) is the number of voxels in the fBA b, in condition α forsubject s.

In the second analysis stage, the mean current density x(t, b; α, s)from each fBA b, for every subject s and condition α, was subjected tospatial PCA analysis of the correlation matrix and varimax rotation

The spatial PCA uses the above-defined fBAs as variables sampled alongthe time epoch for which EEG has been sampled (e.g., 0-1000 ms; 512time-frames), and the inverse solution estimated. Spatial matrices(e.g., each matrix was sized b x t=36×512 elements) for every subjectand condition may be collected, and subjected to PCA analyses, includingthe calculation of the covariance matrix; eigenvalue decomposition andvarimax rotation, in order to maximize factor loadings. In other words,the spatial PCA analysis approximates the mean current density for eachsubject in each condition as

$\begin{matrix}{{{x\left( {{t;\alpha},s} \right)} \approx {{x_{0}\left( {\alpha,s} \right)} + {\sum\limits_{k}{{c_{k}(t)}{x_{k}\left( {\alpha,s} \right)}}}}},} & (5)\end{matrix}$

where here x(t;α,s)∈R³⁶ is a vector, which denotes the time-dependentactivation of the fBAs, x₀(α,s) is their mean activation, and x_(k)(α,s) and c_(k) are the principal components and their correspondingcoefficients (factor loadings) as computed using the principal componentanalysis. See:

-   Arzouan Y, Goldstein A, Faust M. Brainwaves are stethoscopes: ERP    correlates of novel metaphor comprehension. Brain Res 2007; 1160:    69-81.-   Arzouan Y, Goldstein A, Faust M. Dynamics of hemispheric activity    during metaphor comprehension: electrophysiological measures.    NeuroImage 2007; 36: 222-231.-   Chapman R M, McCrary J W. EP component identification and    measurement by principal components analysis. Brain and cognition    1995; 27: 288-310.-   Dien J, Frishkoff G A, Cerbone A, Tucker D M. Parametric analysis of    event-related potentials in semantic comprehension: evidence for    parallel brain mechanisms. Brain research 2003; 15: 137-153.-   Dien J, Frishkoff G A. Principal components analysis of    event-related potential datasets. In: Handy T (ed). Event-Related    Potentials: A Methods Handbook. Cambridge, Mass. MIT Press; 2004.-   Potts G F, Dien J, Hartry-Speiser A L, McDougal L M, Tucker D M.    Dense sensor array topography of the event-related potential to    task-relevant auditory stimuli. Electroencephalography and clinical    neurophysiology 1998; 106: 444-456.-   Rosier F, Manzey D. Principal components and varimax-rotated    components in event-related potential research: some remarks on    their interpretation. Biological psychology 1981; 13: 3-26.-   Ruchkin D S, McCalley M G, Glaser E M. Event related potentials and    time estimation. Psychophysiology 1977; 14: 451-455.-   Spencer K M, Dien J, Donchin E. Spatiotemporal analysis of the late    ERP responses to deviant stimuli. Psychophysiology 2001; 38:    343-358.-   Squires K C, Squires N K, Hillyard S A. Decision-related cortical    potentials during an auditory signal detection task with cued    observation intervals. Journal of experimental psychology 1975; 1:    268-279.-   van Boxtel A, Boelhouwer A J, Bos A R. Optimal EMG signal bandwidth    and interelectrode distance for the recording of acoustic,    electrocutaneous, and photic blink reflexes. Psychophysiology 1998;    35: 690-697.-   download.lww.comwolterskluwer.com/WNR_1_1_2010_03_22_ARZY_1_SDC1.doc.

Nonlinear Dimensionality Reduction

High-dimensional data, meaning data that requires more than two or threedimensions to represent, can be difficult to interpret One approach tosimplification is to assume that the data of interest lie on an embeddednon-linear manifold within the higher-dimensional space. If the manifoldis of low enough dimension, the data can be visualized in thelow-dimensional space. Non-linear methods can be broadly classified intotwo groups: those that provide a mapping (either from thehigh-dimensional space to the low-dimensional embedding or vice versa),and those that just give a visualization. In the context of ML, mappingmethods may be viewed as a preliminary feature extraction step, afterwhich pattern recognition algorithms are applied. Typically, those thatjust give a visualization are based on proximity data—that is, distancemeasurements. Related Linear Decomposition Methods include Independentcomponent analysis (ICA), Principal component analysis (PCA) (alsocalled Karhunen-Loève transform—KLT), Singular value decomposition(SVD), and Factor analysis.

The self-organizing map (SOM, also called Kohonen map) and itsprobabilistic variant generative topographic mapping (GTM) use a pointrepresentation in the embedded space to form a latent variable modelbased on a non-linear mapping from the embedded space to thehigh-dimensional space. These techniques are related to work on densitynetworks, which also are based around the same probabilistic model.

Principal curves and manifolds give the natural geometric framework fornonlinear dimensionality reduction and extend the geometricinterpretation of PCA by explicitly constructing an embedded manifold,and by encoding using standard geometric projection onto the manifold.How to define the “simplicity” of the manifold is problem-dependent,however, it is commonly measured by the intrinsic dimensionality and/orthe smoothness of the manifold. Usually, the principal manifold isdefined as a solution to an optimization problem. The objective functionincludes a quality of data approximation and some penalty terms for thebending of the manifold. The popular initial approximations aregenerated by linear PCA, Kohonen's SOM or autoencoders. The elastic mapmethod provides the expectation-maximization algorithm for principalmanifold learning with minimization of quadratic energy functional atthe “maximization” step.

An autoencoder is a feed-forward neural network which is trained toapproximate the identity function. That is, it is trained to map from avector of values to the same vector. When used for dimensionalityreduction purposes, one of the hidden layers in the network is limitedto contain only a small number of network units. Thus, the network mustlearn to encode the vector into a small number of dimensions and thendecode it back into the original space. Thus, the first half of thenetwork is a model which maps from high to low-dimensional space, andthe second half maps from low to high-dimensional space. Although theidea of autoencoders is quite old, training of deep autoencoders hasonly recently become possible through the use of restricted Boltzmannmachines and stacked denoising autoencoders. Related to autoencoders isthe NeuroScale algorithm, which uses stress functions inspired bymultidimensional scaling and Sammon mappings (see below) to learn anon-linear mapping from the high-dimensional to the embedded space. Themappings in NeuroScale are based on radial basis function networks.

Gaussian process latent variable models (GPLVM) are probabilisticdimensionality reduction methods that use Gaussian Processes (GPs) tofind a lower dimensional non-linear embedding of high dimensional data.They are an extension of the Probabilistic formulation of PCA. The modelis defined probabilistically and the latent variables are thenmarginalized and parameters are obtained by maximizing the likelihood.Like kernel PCA they use a kernel function to form a nonlinear mapping(in the form of a Gaussian process). However, in the GPLVM the mappingis from the embedded (latent) space to the data space (like densitynetworks and GTM) whereas in kernel PCA i is in the opposite direction.It was originally proposed for visualization of high dimensional databut has been extended to construct a shared manifold model between twoobservation spaces. GPLVM and its many variants have been proposedspecially for human motion modeling, e.g., back constrained GPLVM, GPdynamic model (GPDM), balanced GPDM (B-GPDM) and topologicallyconstrained GPDM. To capture the coupling effect of the pose and gaitmanifolds in the gait analysis, a multi-layer joint gait-pose manifoldswas proposed.

Curvilinear component analysis (CCA) looks for the configuration ofpoints in the output space that preserves original distances as much aspossible while focusing on small distances in the output space(conversely to Sammon's mapping which focus on small distances inoriginal space). It should be noticed that CCA, as an iterative learningalgorithm, actually starts with focus on large distances (like theSammon algorithm), then gradually change focus to small distances. Thesmall distance information will overwrite the large distanceinformation, if compromises between the two have to be made. The stressfunction of CCA is related to a sum of right Bregman divergences.Curvilinear distance analysis (CDA) trains a self-organizing neuralnetwork to fit the manifold and seeks to preserve geodesic distances inits embedding. It is based on Curvilinear Component Analysis (whichextended Sammon's mapping), but uses geodesic distances instead.Diffeomorphic Dimensionality Reduction or Diffeomap learns a smoothdiffeomorphic mapping which transports the data onto a lower-dimensionallinear subspace. The method solves for a smooth time indexed vectorfield such that flows along the field which start at the data pointswill end at a lower-dimensional linear subspace, thereby attempting topreserve pair wise differences under both the forward and inversemapping.

Perhaps the most widely used algorithm for manifold learning is Kemelprincipal component analysis (kernel PCA). It is a combination ofPrincipal component analysis and the kernel tick. PCA begins bycomputing the covariance matrix of the M×n Matrix X. It then projectsthe data onto the first k eigenvectors of that matrix. By comparison,KPCA begins by computing the covariance matrix of the data after beingtransformed into a higher-dimensional space. It then projects thetransformed data onto the first k eigenvectors of that matrix, just likePCA. It uses the kernel tick to factor away much of the computation,such that the entire process can be performed without actually computingq)(x). Of course Q must be chosen such that it has a known correspondingkernel.

The Fourier transform (FT) decomposes a function of time (a signal) intothe frequencies that make it up. The Fourier transform of a function oftime is itself a complex-valued function of frequency, whose absolutevalue represents the amount of that frequency present in the originalfunction, and whose complex argument is the phase offset of the basicsinusoid in that frequency. The Fourier transform is called thefrequency domain representation of the original signal. The term Fouriertransform refers to both the frequency domain representation and themathematical operation that associates the frequency domainrepresentation to a function of time. The Fourier transform is notlimited to functions of time, but in order to have a unified language,the domain of the original function is commonly referred to as the timedomain. For many functions of practical interest, one can define anoperation that reverses this: the inverse Fourier transformation, alsocalled Fourier synthesis, of a frequency domain representation combinesthe contributions of all the different frequencies to recover theoriginal function of time. See, en.wikipedia.org/wiki/Fourier_transform.

The Fourier transform of a finite Borel measure μ on

^(n) is given by: {circumflex over (μ)}(ζ)=

e^(−2πixζ)dμ. This transform continues to enjoy many of the propertiesof the Fourier transform of integrable functions. One notable differenceis that the Riemann-Lebesgue lemma fails for measures. In the case thatdμ=f(x) dx, then the formula above reduces to the usual definition forthe Fourier transform of f. In the case that p is the probabilitydistribution associated to a random variable X, the Fourier-Stieltjestransform is closely related to the characteristic function, but thetypical conventions in probability theory take e^(ixζ) instead ofe^(−2πixζ). In the case when the distribution has a probability densityfunction this definition reduces to the Fourier transform applied to theprobability density function, again with a different choice ofconstants. The Fourier transform may be used to give a characterizationof measures. Bochner's theorem characterizes which functions may ariseas the Fourier-Stieljes transform of a positive measure on the circle.Furthermore, the Dirac delta function, although not a function, is afinite Borel measure. Its Fourier transform is a constant function(whose specific value depends upon the form of the Fourier transformused). See Pinsky, Mark (2002), Introduction to Fourier Analysis andWavelets, Brooks/Cole, ISBN 978-0-534-37660-4; Katznelson, Yitzhak(1976), An Introduction to Harmonic Analysis, Dover, ISBN978-0-486-63331-2.

The Fourier transform is also a special case of Gelfand transform. Inthis particular context, i is closely related to the Pontryagin dualitymap. Given an abelian locally compact Hausdorff topological group G, asbefore we consider space L¹(G), defined using a Haar measure. Withconvolution as multiplication, L¹(G) is an abelian Banach algebra.Taking the completion with respect to the largest possibly C*-norm givesits enveloping C*-algebra, called the group C*-algebra C*(G) of G. (AnyC*-norm on L¹(G) is bounded by the L¹ norm, therefore their supremumexists.) 8 is the involution operator. Given any abelian C*-algebra A,the Gelfand transform gives an isomorphism between A and C₀(A{circumflexover ( )}), where A{circumflex over ( )} is the multiplicative linearfunctionals, i.e. one-dimensional representations, on A with theweak-*topology. The multiplicative linear functionals of C*(G), aftersuitable identification, are exactly the characters of G, and theGelfand transform, when restricted to the dense subset L¹(G) is theFourier-Pontryagin transform.

The Laplace transform is very similar to the Fourier transform. Whilethe Fourier transform of a function is a complex function of a realvariable (frequency), the Laplace transform of a function is a complexfunction of a complex variable. Laplace transforms are usuallyrestricted to functions of t with t≥0. A consequence of this restrictionis that the Laplace transform of a function is a holomorphic function ofthe variable s. The Laplace transform of a distribution is generally awell-behaved function. As a holomorphic function, the Laplace transformhas a power series representation. This power series expresses afunction as a linear superposition of moments of the function. TheLaplace transform is invertible on a large class of functions. Theinverse Laplace transform takes a function of a complex variable s(often frequency) and yields a function of a real variable t (time).Given a simple mathematical or functional description of an input oroutput to a system, the Laplace transform provides an alternativefunctional description that often simplifies the process of analyzingthe behavior of the system, or in synthesizing a new system based on aset of specifications. So, for example, Laplace transformation from thetime domain to the frequency domain transforms differential equationsinto algebraic equations and convolution into multiplication. See,en.wikipedia.org/wiki/Laplace_transform.

The short-time Fourier transform (STFT), is a Fourier-related transformused to determine the sinusoidal frequency and phase content of localsections of a signal as it changes over time. In practice, the procedurefor computing STFTs is to divide a longer time signal into shortersegments of equal length and then compute the Fourier transformseparately on each shorter segment. This reveals the Fourier spectrum oneach shorter segment. One then usually plots the changing spectra as afunction of time. The signal may be windowed using, e.g., a Hann windowor a Gaussian window. See,en.wikipedia.org/wiki/Short-time_Fourier_transform.

The fractional Fourier transform (FRFT), is a generalization of theclassical Fourier transform. The FRFT of a signal can also beinterpreted as a decomposition of the signal in terms of chirps. TheFRFT can be used to define fractional convolution, correlation, andother operations, and can also be further generalized into the linearcanonical transformation (LCT). See:en.wikipedia.orgiwikilFractional_Fourier_transform.

-   Almeida, Luis B. “The fractional Fourier transform and    time-frequency representations.” IEEE Transactions on signal    processing 42, no. 11 (1994): 3084-3091.-   Bailey, David H., and Paul N. Swarztrauber. “The fractional Fourier    transform and applications.” SIAM review 33, no. 3 (1991): 389-404.

Candan, Cagatay, M. Alper Kutay, and Haldun M. Ozaktas. “The discretefractional Fourier transform.” IEEE Transactions on signal processing48, no. 5 (2000): 1329-1337.

-   Lohmann, Adolf W. “Image rotation, Wigner rotation, and the    fractional Fourier transform.” JOSA A 10, no. 10 (1993): 2181-2186.-   Ozaktas, Haldun M., and David Mendlovic. “Fourier transforms of    fractional order and their optical interpretation.” Optics    Communications 101, no. 3-4 (1993): 163-169.-   Ozaktas, Haldun M., and M. Alper Kutay. “The fractional    Fouriertransform.” In Control Conference (ECC), 2001 European, pp.    1477-1483. IEEE, 2001.-   Ozaktas, Haldun M., Orhan Arikan, M. Alper Kutay, and Gozde Bozdagt    “Digital computation of the fractional Fourier transform.” IEEE    Transactions on signal processing 44, no. 9 (1996): 2141-2150.-   Pei, Soo-Chang, Min-Hung Yeh, and Chien-Cheng Tseng. “Discrete    fractional Fourier transform based on orthogonal projections.” IEEE    Transactions on Signal Processing 47, no. 5 (1999): 1335-1348.-   Qi, Lin, Ran Tao, Siyong Zhou, and Yue Wang. “Detection and    parameter estimation of multicomponent LFM signal based on the    fractional Fourier transform.” Science in China series F:    information sciences 47, no. 2 (2004): 184.-   Tao, Ran, Yan-Lei Li, and Yue Wang. “Short-time fractional Fourier    transform and its applications.” IEEE Transactions on Signal    Processing 58, no. 5 (2010): 2568-2580.-   Xia, Xiang-Gen. “On bandlimited signals with fractional Fourier    transform.” IEEE Signal Processing Letters 3, no. 3 (1996): 72-74.-   Zayed, Ahmed I. “A convolution and product theorem for the    fractional Fourier transform.” IEEE Signal processing letters 5, no.    4 (1998): 101-103.-   Zayed, Ahmed I. “On the relationship between the Fourier and    fractional Fourier transforms.” IEEE signal processing letters 3,    no. 12 (1996): 310-311.

Laplacian Eigenmaps, (also known as Local Linear Eigenmaps, LLE) arespecial cases of kernel PCA, performed by constructing a data-dependentkernel matrix. KPCA has an internal model, so it can be used to mappoints onto its embedding that were not available at training time.Laplacian Eigenmaps uses spectral techniques to perform dimensionalityreduction. This technique relies on the basic assumption that the datalies in a low-dimensional manifold in a high-dimensional space. Thisalgorithm cannot embed out of sample points, but techniques based onReproducing kernel Hilbert space regularization exist for adding thiscapability. Such techniques can be applied to other nonlineardimensionality reduction algorithms as well. Traditional techniques likeprincipal component analysis do not consider the intrinsic geometry ofthe data. Laplacian eigenmaps builds a graph from neighborhoodinformation of the data set Each data point serves as a node on thegraph and connectivity between nodes is governed by the proximity ofneighboring points (using e.g. the k-nearest neighbor algorithm). Thegraph thus generated can be considered as a discrete approximation ofthe low-dimensional manifold in the high-dimensional space. Minimizationof a cost function based on the graph ensures that points close to eachother on the manifold are mapped close to each other in thelow-dimensional space, preserving local distances. The eigenfunctions ofthe Laplace-Beltrami operator on the manifold serve as the embeddingdimensions, since under mild conditions this operator has a countablespectrum that is a basis for square integrable functions on the manifold(compare to Fourier series on the unit circle manifold). Attempts toplace Laplacian eigenmaps on solid theoretical ground have met with somesuccess, as under certain nonrestrictive assumptions, the graphLaplacian matrix has been shown to converge to the Laplace-Beltramioperator as the number of points goes to infinity. In classificationapplications, low dimension manifolds can be used to model data classeswhich can be defined from sets of observed instances. Each observedinstance can be described by two independent factors termed ‘content’and ‘style’, where ‘content’ is the invariant factor related to theessence of the class and ‘style’ expresses variations in that classbetween instances. Unfortunately, Laplacian Eigenmaps may fail toproduce a coherent representation of a class of interest when trainingdata consist of instances varying significantly in terms of style. Inthe case of classes which are represented by multivariate sequences,Structural Laplacian Eigenmaps has been proposed to overcome this issueby adding additional constraints within the Laplacian Eigenmapsneighborhood information graph to better reflect the intrinsic structureof the class. More specifically, the graph is used to encode both thesequential structure of the multivariate sequences and, to minimizestylistic variations, proximity between data points of differentsequences or even within a sequence, if it contains repetitions. Usingdynamic time warping, proximity is detected by finding correspondencesbetween and within sections of the multivariate sequences that exhibithigh similarity.

Like LLE, Hessian LLE is also based on sparse matrix techniques. Ittends to yield results of a much higher quality than LLE. Unfortunately,it has a very costly computational complexity, so it is not well-suitedfor heavily sampled manifolds. It has no internal model. Modified LLE(MLLE) is another LLE variant which uses multiple weights in eachneighborhood to address the local weight matrix conditioning problemwhich leads to distortions in LLE maps. MLLE produces robust projectionssimilar to Hessian LLE, but without the significant additionalcomputational cost.

Manifold alignment takes advantage of the assumption that disparate datasets produced by similar generating processes will share a similarunderlying manifold representation. By learning projections from eachoriginal space to the shared manifold, correspondences are recovered andknowledge from one domain can be transferred to another. Most manifoldalignment techniques consider only two data sets, but the conceptextends to arbitrarily many initial data sets. Diffusion maps leveragesthe relationship between heat diffusion and a random walk (MarkovChain); an analogy is drawn between the diffusion operator on a manifoldand a Markov transition matrix operating on functions defined on thegraph whose nodes were sampled from the manifold. Relational perspectivemap is a multidimensional scaling algorithm. The algorithm finds aconfiguration of data points on a manifold by simulating amulti-particle dynamic system on a closed manifold, where data pointsare mapped to particles and distances (or dissimilarity) between datapoints represent a repulsive force. As the manifold gradually grows insize the multi-particle system cools down gradually and converges to aconfiguration that reflects the distance information of the data points.Local tangent space alignment (LTSA) is based on the intuition that whena manifold is correctly unfolded, all of the tangent hyperplanes to themanifold will become aligned. It begins by computing the k-nearestneighbors of every point. It computes the tangent space at every pointby computing the d-first principal components in each localneighborhood. It then optimizes to find an embedding that aligns thetangent spaces. Local Multidimensional Scaling performs multidimensionalscaling in local regions, and then uses convex optimization to fit allthe pieces together.

Maximum Variance Unfolding was formerly known as Semidefinite Embedding.The intuition for this algorithm is that when a manifold is properlyunfolded, the variance over the points is maximized. This algorithm alsobegins by finding the k-nearest neighbors of every point. It then seeksto solve the problem of maximizing the distance between allnon-neighboring points, constrained such that the distances betweenneighboring points are preserved. Nonlinear PCA (NLPCA) usesbackpropagation to train a multi-layer perceptron (MLP) to fit to amanifold. Unlike typical MLP training, which only updates the weights,NLPCA updates both the weights and the inputs. That is, both the weightsand inputs are treated as latent values. After training, the latentinputs are a low-dimensional representation of the observed vectors, andthe MLP maps from that low-dimensional representation to thehigh-dimensional observation space. Manifold Sculpting uses graduatedoptimization to find an embedding. Like other algorithms, it computesthe k-nearest neighbors and ties to seek an embedding that preservesrelationships in local neighborhoods. It slowly scales variance out ofhigher dimensions, while simultaneously adjusting points in lowerdimensions to preserve those relationships.

Ruffini (2015) discusses Multichannel transcranial current stimulation(tCS) systems that offer the possibility of EEG-guided optimized,non-invasive brain stimulation. A tCS electric field realistic brainmodel is used to create a forward “lead-field” matrix and, from that, anEEG inverter is employed for cortical mapping. Starting from EEG, 2Dcortical surface dipole fields are defined that could produce theobserved EEG electrode voltages.

Schestatsky et al. (2017) discuss transcranial direct currentstimulation (tDCS), which stimulates through the scalp with a constantelectric current that induces shifts in neuronal membrane excitability,resulting in secondary changes in cortical activity. Although tDCS hasmost of its neuromodulatory effects on the underlying cortex, tDCSeffects can also be observed in distant neural networks. Concomitant EEGmonitoring of the effects of tDCS can provide valuable information onthe mechanisms of tDCS. EEG findings can be an important surrogatemarker for the effects of tDCS and thus can be used to optimize itsparameters. This combined EEG-tDCS system can also be used forpreventive treatment of neurological conditions characterized byabnormal peaks of cortical excitability, such as seizures. Such a systemwould be the basis of a non-invasive closed-loop device. tDCS and EEGcan be used concurrently. See:

-   Albert, Jacobo, Sara López-Martin, José Antonio Hinojosa, and Luis    Carretié. “Spatiotemporal characterization of response inhibition.”    Neuroimage 76 (2013): 272-281.-   Arzouan Y, Goldstein A, Faust M. Brainwaves are stethoscopes: ERP    correlates of novel metaphor comprehension. Brain Res 2007; 1160:    69-81.-   Arzouan Y, Goldstein A, Faust M. Dynamics of hemispheric activity    during metaphor comprehension: electrophysiological measures.    NeuroImage 2007; 36: 222-231.-   Arzy, Shahar, Yossi Arzouan, Esther Adi-Japha, Sorin Solomon, and    Olaf Blanke. “The ‘intrinsic’ system in the human cortex and    self-projection: a data driven analysis.” Neuroreport 21, no. 8    (2010): 569-574.-   Bao, Xuecai, Jinli Wang, and Jianfeng Hu. “Method of individual    identification based on electroencephalogram analysis.” In New    Trends in Information and Service Science, 2009. NISS'09.    International Conference on, pp. 390-393. IEEE, 2009.-   Bhattacharya, Joydeep. “Complexity analysis of spontaneous EEG.”    Acta neurobiologiae experimentalis 60, no. 4 (2000): 495-502.-   Chapman R M, McCrary J W. EP component identification and    measurement by principal components analysis. Brain and cognition    1995; 27: 288-310.-   Clementz, Brett A, Stefanie K. Barber, and Jacqueline R. Dzau.    “Knowledge of stimulus repetition affects the magnitude and spatial    distribution of low-frequency event-related brain potentials.”    Audiology and Neurotology 7, no. 5 (2002): 303-314.-   Dien J, Frishkoff G A, Cerbone A, Tucker D M. Parametric analysis of    event-related potentials in semantic comprehension: evidence for    parallel brain mechanisms. Brain research 2003; 15: 137-153.-   Dien J, Frishkoff G A. Principal components analysis of    event-related potential datasets. In: Handy T (ed). Event-Related    Potentials: A Methods Handbook. Cambridge, Mass. MIT Press; 2004.-   Elbert, T. “IIIrd Congress of the Spanish Society of    Psychophysiology.” Journal of Psychophysiology 17 (2003): 39-53.-   Groppe, David M., Scott Makeig, Marta Kutas, and S. Diego.    “Independent component analysis of event-related potentials.”    Cognitive science online 6, no. 1 (2008): 1-44.-   Have, Mid-Ventrolateral Prefrontal Cortex. “Heschl's Gyrus,    Posterior Superior Temporal Gyrus.” J Neurophysiol 97 (2007):    2075-2082.-   Hinojosa, J. A., J. Albert, S. López-Martin, and L. Carretié.    “Temporospatial analysis of explicit and implicit processing of    negative content during word comprehension.” Brain and cognition 87    (2014): 109-121.-   Jarchi, Delaram, Saeid Sanei, Jose C. Principe, and Bahador    Makkiabadi. “A new spatiotemporal filtering method for single-trial    estimation of correlated ERP subcomponents.” IEEE Transactions on    Biomedical Engineering 58, no. 1 (2011): 132-143.-   John, Erwin Roy. “A field theory of consciousness.” Consciousness    and cognition 10, no. 2 (2001): 184-213.-   Johnson, Mark H., Michelle de Haan, Andrew Oliver, Warwick Smith,    Haralambos Hatzakis, Leslie A. Tucker, and Gergely Csibra.    “Recording and analyzing high-density event-related potentials with    infants using the Geodesic Sensor Net” Developmental Neuropsychology    19, no. 3 (2001): 295-323.-   Jung, Tzyy-Ping, and Scott Makeig. “Mining Electroencephalographic    Data Using Independent ComponentAnalysis.” EEG Journal (2003).-   Kashyap, Rajan. “Improved localization of neural sources and    dynamical causal modelling of latency-corrected event related brain    potentials and applications to face recognition and priming.”    (2015).-   Klawohn, Julia, Anja Riesel, Rosa Grützmann, Norbert Kathmann, and    Tanja Endrass. “Performance monitoring in obsessive-compulsive    disorder A temporo-spatial principal component analysis.” Cognitive,    Affective, & Behavioral Neuroscience 14, no. 3 (2014): 983-995.-   Lister, Jennifer J., Nathan D. Maxfield, and Gabriel J. Pitt    “Cortical evoked response to gaps in noise: within-channel and    across-channel conditions.” Ear and hearing 28, no. 6 (2007): 862.-   Maess, Burkhard, Angela D. Friederici, Markus Damian, Antje S.    Meyer, and Willem J M Levelt “Semantic category interference in    overt picture naming: Sharpening current density localization by    PCA.” Journal of cognitive neuroscience 14, no. 3 (2002): 455-462.-   Makeig, Scott, Marissa Westerfield, Jeanne Townsend, Tzyy-Ping Jung,    Eric Courchesne, and Terrence J. Sejnowski. “Functionally    independent components of early event-related potentials in a visual    spatial attention task.” Philosophical Transactions of the Royal    Society B: Biological Sciences 354, no. 1387 (1999): 1135-1144.-   Matsuda, Izumi, Hiroshi Nittono, Akihisa Hirota, Tokihiro Ogawa, and    Noriyoshi Takasawa. “Event-related brain potentials during the    standard autonomic-based concealed information test” International    Journal of Psychophysiology 74, no. 1 (2009): 58-68.-   Mazaheri, Ali, and Terence W. Picton. “EEG spectral dynamics during    discrimination of auditory and visual targets.” Cognitive Brain    Research 24, no. 1 (2005): 81-96.-   Pirmoradi, Mona, Boutheina Jemel, Anne Gallagher, Julie Tremblay,    Fabien D'Hondt, Dang Khoa Nguyen, Renee Beland, and Maryse Lassonde.    “Verbal memory and verbal fluency tasks used for language    localization and lateralization during magnetoencephalography.”    Epilepsy research 119 (2016): 1-9.-   Potts G F, Dien J, Hartry-Speiser A L, McDougal L M, Tucker D M.    Dense sensor array topography of the event-related potential to    task-relevant auditory stimuli. Electroencephalography and clinical    neurophysiology 1998; 106: 444-456.-   Rosier F, Manzey D. Principal components and varimax-rotated    components in event-related potential research: some remarks on    their interpretation. Biological psychology 1981; 13: 3-26.-   Ruchkin D S, McCalley M G, Glaser E M. Event related potentials and    time estimation. Psychophysiology 1977; 14: 451-455.-   Schroder, Hans S., James E. Glazer, Ken P. Bennett, Tim P. Moran,    and Jason S. Moser. “Suppression of error-preceding brain activity    explains exaggerated error monitoring in females with worry.”    Biological psychology 122 (2017): 33-41.-   Spencer K M, Dien J, Donchin E. Spatiotemporal analysis of the late    ERP responses to deviant stimuli. Psychophysiology 2001; 38:    343-358.-   Squires K C, Squires N K, Hillyard S A. Decision-related cortical    potentials during an auditory signal detection task with cued    observation intervals. Journal of experimental psychology 1975; 1:    268-279.-   van Boxtel A, Boelhouwer A J, Bos A R. Optimal EMG signal bandwidth    and interelectrode distance for the recording of acoustic,    electrocutaneous, and photic blink reflexes. Psychophysiology 1998;    35: 690-697.-   Veen, Vincent van, and Cameron S. Carter. “The timing of    action-monitoring processes in the anterior cingulate cortex.”    Journal of cognitive neuroscience 14, no. 4 (2002): 593-602.-   Wackermann, Jiri. “Towards a quantitative characterisation of    functional states of the brain: from the non-linear methodology to    the global linear description.” International Journal of    Psychophysiology 34, no. 1 (1999): 65-80.

EEG analysis approaches have emerged, in which event-related changes inEEG dynamics in single event-related data records are analyzed. SeeAllen D. Malony et al., Computational Neuroinformatics for IntegratedElectromagnetic Neuroimaging and Analysis, PAR-99-138. Pfurtscheller,reported a method for quantifying the average transient suppression ofalpha band (circa 10-Hz) activity following stimulation. Event-relateddesynchronization (ER D, spectral amplitude decreases), andevent-related synchronization (ERS, spectral amplitude increases) areobserved in a variety of narrow frequency bands (4-40 Hz) which aresystematically dependent on task and cognitive state variables as wellas on stimulus parameters. Makeig (1993) was reported event-relatedchanges in the full EEG spectrum, yielding a 2-D time/frequency measurehe called the event-related spectral perturbation (ERSP). This methodavoided problems associated with analysis of a priori narrow frequencybands, since bands of interest for the analysis could be based onsignificant features of the complete time/frequency transform.Rappelsburger et al. introduced event-related coherence (ERCOH). A widevariety of other signal processing measures have been tested for use onEEG and/or MEG data, including dimensionality measures based on chaostheory and the bispectrum. Use of neural networks has also been proposedfor EEG pattern recognition applied to clinical and practical problems,though usually these methods have not been employed with an aim ofexplicitly modeling the neurodynamics involved. Neurodynamics is themobilization of the nervous system as an approach to physical treatment.The method relies on influencing pain and other neural physiology viamechanical treatment of neural tissues and the non-neural structuressurrounding the nervous system. The body presents the nervous systemwith a mechanical interface via the musculoskeletal system. Withmovement, the musculoskeletal system exerts non-uniform stresses andmovement in neural tissues, depending on the local anatomical andmechanical characteristics and the pattern of body movement. Thisactivates an array of mechanical and physiological responses in neuraltissues. These responses include neural sliding, pressurization,elongation, tension and changes in intraneural microcirculation, axonaltransport and impulse traffic.

The availability of and interest in larger and larger numbers of EEG(and MEG) channels led immediately to the question of how to combinedata from different channels. Donchin advocated the use of linear factoranalysis methods based on principal component analysis (PCA) for thispurpose. Temporal PCA assumes that the time course of activation of eachderived component is the same in all data conditions. Because this isunreasonable for many data sets, spatial PCA (usually followed by acomponent rotation procedure such as Varimax or Promax) is ofpotentially greater interest. To this end, several variants of PCA havebeen proposed for ERP decomposition.

Bell and Sejnowski published an iterative algorithm based on informationtheory for decomposing linearly mixed signals into temporallyindependent by minimizing their mutual information. First approaches toblind source separation minimized third and fourth-order correlationsamong the observed variables and achieved limited success insimulations. A generalized approach uses a simple neural networkalgorithm that used joint information maximization or ‘infomax’ as atraining criterion. By using a compressive nonlinearity to transform thedata and then following the entropy gradient of the resulting mixtures,ten recorded voice and music sound sources were unmixed. A similarapproach was used for performing blind deconvolution, and the ‘infomax’method was used for decomposition of visual scenes.

-   EEG source analysis may be accomplished using various techniques.    Grech, Roberta, Tracey Cassar, Joseph Muscat, Kenneth P. Camilleri,    Simon G. Fabri, Michalis Zervakis, Petros Xanthopoulos, Vangelis    Sakkalis, and Bart Vanrumste. “Review on solving the inverse problem    in EEG source analysis.” Journal of neuroengineering and    rehabilitation 5, no. 1 (2008): 25.-   De Munck J C, Van Dijk B W, Spekreijse H. Mathematical Dipoles are    Adequate to Describe Realistic Generators of Human Brain Activity.    IEEE Transactions on Biomedical Engineering. 1988; 35:960-966. doi:    10.1109/10.8677.-   Hallez H, Vanrumste B, Grech R, Muscat J, De Clercq W, VergultA,    D'Asseler Y, Camilleri K P, Fabri S G, Van Huffel S, Lemahieu I.    Review on solving the forward problem in EEG source analysis. J. of    NeuroEngineering and Rehabilitation. 2007; 4-   Whittingstall K, Stroink G, Gates L, Connolly J F, Finley A. Effects    of dipole position, orientation and noise on the accuracy of EEG    source localization. Biomedical Engineering Online. 2003; 2    www.biomedical-engineeing-online.com/content/2/1/14-   Baillet S, Gamero L. A Bayesian Approach to Introducing    Anatomo-Functional Priors in the EEG/MEG Inverse Problem. IEEE    Transactions on Biomedical Engineering. 1997; 44:374-385. doi:    10.1109/10.568913.-   Pascual-Marqui R D. Review of Methods for Solving the EEG Inverse    Problem. International Journal of Bioelectromagnetism. 1999;    1:75-86.-   Baillet S, Mosher J C, Leahy R M. Electromagnetic Brain Mapping.    IEEE Signal Processing Magazine. 2001; 18:14-30. doi:    10.1109/79.962275.-   Groetsch W. Inverse Problems in the Mathematical Sciences. Vieweg.    1993.-   Hansen P C. Rank-Deficient and Discrete III-Posed Problems. SIAM.    1998.-   Vogel C R. Computational Methods for Inverse Problems. SIAM. 2002.-   De Munck J C. The estimation of time varying dipoles on the basis of    evoked potentials. Electroencephalography and Clinical    Neurophysiology. 1990; 77:156-160. doi:    10.1016/0168-5597(90)90032-9.-   Rodriguez-Rivera A, Van Veen B D, Wakai R T. Statistical Performance    Analysis of Signal Variance-Based Dipole Models for MEG/EEG Source    Localization and Detection. IEEE Transactions on Biomedical    Engineering. 2003; 50:137-149. doi: 10.1109/TBME.2002.807661.-   Liu A K, Dale A M, Belliveau J W. Monte Carlo Simulation Studies of    EEG and MEG Localization Accuracy. Human Brain Mapping. 2002;    16:47-62. doi: 10.1002/hbm.10024.-   Schmidt D M, George J S, Wood C C. Bayesian Inference Applied to the    Electromagnetic Inverse Problem. Progress Report 1997-1998, Physics    Division. 2002.-   Dale A, Sereno M. Improved Localization of Cortical Activity By    Combining EEG and MEG with MRI Cortical Surface Reconstruction: A    Linear Approach. Journal of Cognitive Neuroscience. 1993; 5:162-176.    doi: 10.1162/jocn.1993.5.2.162.-   Gavit L, Baillet S, Mangin J F, Pescatore J, Gamero L. A    Multiresolution Framework to MEG/EEG Source Imaging. IEEE    Transactions on Biomedical Engineering. 2001; 48:1080-1087. doi:    10.1109/10.951510.-   Kreyszig E. Introductory Functional Analysis With Applications. John    Wiley & Sons, Inc; 1978.-   Silva C, Malez J C, Trindade E, Aniaga A, Ducla-Soares E. Evaluation    of L1 and L2 minimum-norm performances on EEG localizations.    Clinical Neurophysiology. 2004; 115:1657-1668. doi:    10.1016j.clinph.2004.02.009.-   Chellapa R, Jain A, Eds. Markov Random Fields: Theory and    Applications. Academic Press; 1991.-   Li S Z. Markov Random Field Modeling in Computer Vision. New York:    Springer-Verlag; 1995.-   Liu H, Gao X, Schimpf P H, Yang F, Gao S. A Recursive Algorithm for    the Three-Dimensional Imaging of Brain Electric Activity: Shrinking    LORETA-FOCUSS. IEEE Transactions on Biomedical Engineering. 2004;    51:1794-1802. doi: 10.1109/TBME.2004.831537.-   Hansen P C. Regularization Tools: A Matlab package for Analysis and    Solution of Discrete III-Posed Problems. Numerical Algorithms. 1994;    6:1-35. doi: 10.1007/BF02149761.-   Hansen P C. The L-curve and its use in the numerical treatment of    inverse problems. In: Johnston P, editor. Computational Inverse    Problems in Electrocardiology. WIT Press; 2001. pp. 119-142.-   Cheng L K, Bodley J M, Pullan A J. Comparison of Potential- and    Activation-Based Formulations for the Inverse Problem of    Electrocardiology. IEEE Transactions on Biomedical Engineering.    2003; 50:11-22. doi: 10.1109/TBME.2002.807326.-   Lian J, Yao D, He B P. A New Method for Implementaion of    Regularization in Cortical Potential Imaging. Proceedings of the    20th Annual International Conference of the IEEE Engineering in    Medicine and Biology Society. 1998; 20-   Ding L, He B. 3-Dimensional Brain Source Imaging by Means of    Laplacian Weighted Minimum Norm Estimate in a Realistic Geometry    Head Model. Proceedings of the 2005 IEEE Engineering in Medicine and    Biology 27th Annual Conference. 1995.-   De Peralta-Menendez R G, Gonzalez-Andino S L. A Critical Analysis of    Linear Inverse Solutions to the Neuroelectromagnetic Inverse    Problem. IEEE Transactions on Biomedical Engineering. 1998;    45:440-448. doi: 10.1109/10.664200.-   Baillet S. PhD thesis. University of Paris-ParisXI, Orsay,    France; 1998. Toward Functional Brain Imaging of Cortical    Electrophysiology Markovian Models for Magneto and    Electroencephalogram Source Estimation and Experimental Assessments.-   Gençer N G, Williamson S J. Characterization of Neural Sources with    Bimodal Truncated SVD Pseudo-Inverse for EEG and MEG Measurements.    IEEE Transactions on Biomedical Engineering. 1998; 45:827-838. doi:    10.1109/10.686790.-   Gorodnitsky I F, Rao B D. Sparse Signal Reconstruction from Limited    Data Using FOCUSS: A Re-weighted Minimum Norm Algorithm. IEEE    Transactions on Signal Processing. 1997; 45:600-615. doi:    10.1109/78.558475.-   Gorodnitsky I F, George J S, Rao B D. Neuromagnetic source imaging    with FOCUSS: a recursive weighted minimum norm algorithm.    Electroencephalography and clinical Neurophysiology. 1995:231-251.    doi: 10.1016/0013-4694(95)00107-A.-   Xin G, Xinshan M, Yaoqin X. A new algorithm for EEG source    reconstruction based on LORETA by contracting the source region.    Progress in Natural Science. 2002; 12:859-862.-   Pascual-Marqui R D. Standardized low resolution brain    electromagnetic tomography (sLORETA):technical details. Methods and    Findings in Experimental & Clinical Pharmacology. 2002; 24D:5-12.-   Dale A, Liu A, Fischl B, Buckner R, Belliveau J, Lewine J,    Halgren E. Dynamic statistical parametric mapping: combining fMRI    and MEG for high-resolution imaging of cortical activity. Neuron.    2000; 26:55-67. doi: 10.1016/S0896-6273(00)81138-1.-   Valdes-Sosa P, Marti F, Casanova R. Variable Resolution    Electric-Magnetic Tomography. Cuban Neuroscience Center, Havana    Cuba.-   Galka A, Yamashita O, Ozaki T, Biscay R, Valdés-Sosa P. A solution    to the dynamical inverse problem of EEG generation using    spatiotemporal Kalman filtering. NeuroImage. 2004; 23:435-453. doi:    10.1016/j.neuroimage.2004.02.022.-   Riera J J, Valdes P A, Fuentes M E, Ohaniz Y. Explicit Backus and    Gilbert EEG Inverse Solution for Spherical Symmetry. Department of    Neurophysics, Cuban Neuroscience Center, Havana, Cuba. 2002.-   De Peralta-Menendez R G, Hauk O, Gonzalez-Andino S, Vogt H,    Michel C. Linear inverse solutions with optimal resolution kernels    applied to electromagnetic tomography. Human Brain Mapping. 1997;    5:454-467. doi:    10.1002/(SICI)1097-0193(1997)5:6<454::AID-HBM6>3.0.CO;2-2.-   De Peralta-Menendez R G, Gonzalez-Andino S L. Comparison of    algorithms for the localization of focal sources: evaluaton with    simulated data and analysis of experimental data. International    Journal of Bioelectromagnetism. 2002; 4-   Michel C M, Murray M M, Lantz G, Gonzalez S, Spinelli L, De Peralta    R G. EEG source imaging. Clinical Neurophysiology. 2004;    115:2195-2222. doi: 10.1016j.clinph.2004.06.001.-   De Peralta Menendez R G, Murray M M, Michel C M, Martuzzi R,    Gonzalez-Andino S L. Electrical neuroimaging based on biophysical    constraints. NeuroImage. 2004; 21:527-539. doi:    10.1016/j.neuroimage.2003.09.051.-   Liu H, Schimpf P H, Dong G, Gao X, Yang F, Gao S. Standardized    Shrinking LORETA-FOCUSS (SSLOFO): A New Algorithm for    Spatio-Temporal EEG Source Reconstruction. IEEE Transactions on    Biomedical Engineering. 2005; 52:1681-1691. doi:    10.1109/TBME.2005.855720.-   Schimpf P H, Liu H, Ramon C, Haueisen J. Efficient Electromagnetic    Source Imaging With Adaptive Standardized LORETA/FOCUSS. IEEE    Transactions on Biomedical Engineering. 2005; 52:901-908. doi:    10.1109/TBME.2005.845365.-   Cuffin B N. A Method for Localizing EEG Head Models. IEEE    Transactions on Biomedical Engineering. 1995; 42:68-71. doi:    10.1109/10.362917.-   Finke S, Gulrajani R M, Gotman J. Conventional and Reciprocal    Approaches to the Inverse Dipole Localization Problem of    Electroencephalography. IEEE Transactions on Biomedical Engineering.    2003; 50:657-666. doi: 10.1109/TBME.2003.812198.-   Press W H, Teukolsky S A, Vetterling W T, Flannery B P. Numerical    Recipes in C. 2^(n) Ed. Cambridge U. Press; 1992.-   Vanrumste B, Van Hoey G, Walle R Van de, Van Hese P, D'HavéM, Boon    P, Lemahieu I. The Realistic Versus the Spherical Head Model in EEG    Dipole Source Analysis in the Presence of Noise. Proceedings-23rd    Annual Conference-IEEE/EMBS, Istanbul, Turkey. 2001.-   Miga M I, Kemer T E, Darcey T M. Source Localization Using a    Current-Density Minimization Approach. IEEE Transactions on    Biomedical Engineering. 2002; 49:743-745. doi:    10.1109/TBME.2002.1010860.-   Uutela K, Hämäaläinen M, Salmelin R. Global Optimization in the    Localization of Neuromagnetic Sources. IEEE Transactions on    Biomedical Engineering. 1998; 45:716-723. doi: 10.1109/10.678606.-   Van Veen B D, Van Drongelen W, Yuchtman M, Suzuki A. Localization of    Brain Electrical Activity via Linearly Constrained Minimum Variance    Spatial Filtering. IEEE Transactions on Biomedical Engineering.    1997; 44:867-880. doi: 10.1109/10.623056.-   Sekihara K, Nagarajan S, Poeppe D, Miyashita Y. Reconstrusting    Spatio-Temporal Activities of Neural Sources from    Magnetoencephalographic Data Using a Vector Beamformer. IEEE    International Conference on Acoustics, Speech and Signal Processing    Proceedings. 2001; 3:2021-2024.-   Mosher J C, Lewis P S, Leahy R M. Multiple Dipole Modeling and    Localization from Spatio-Temporal MEG Data. IEEE Transactions on    Biomedical Engineering. 1992; 39:541-557. doi: 10.1109/10.141192.-   Maris E. A Resampling Method for Estimating the Signal Subspace of    Spatio-Temporal EEG/MEG Data. IEEE Transactions on Biomedical    Engineering. 2003; 50:935-949. doi: 10.1109/TBME.2003.814293.-   Mosher J C, Leahy R M. Recursive MUSIC: A Framework for EEG and MEG    Source Localization. IEEE Transactions on Biomedical Engineering.    1998; 45:1342-1354. doi: 10.1109/10.725331.-   Mosher J C, Leahy R M. Source Localization Using Recursively Applied    and Projected (RAP) MUSIC. IEEE Transactions on Signal Processing.    1999; 47:332-340. doi: 10.1109/78.740118.-   Ermer J J, Mosher J C, Huang M, Leahy R M. Paired MEG Data Set    Source Localization Using Recursively Applied and Projected (RAP)    MUSIC. IEEE Transactions on Biomedical Engineering. 2000;    47:1248-1260. doi: 10.1109/10.867959.-   Xu X, Xu B, He B. An alternative subspace approach to EEG dipole    source localization. Physics in Medicine and Biology. 2004;    49:327-343. doi: 10.1088/0031-9155/49/2/010.-   Robert C, Gaudy J, Limoge A. Electroencephalogram processing using    neural networks. Clinical Neurophysiology. 2002; 113:694-701. doi:    10.1016/S1388-2457(02)00033-0.-   Tun A K, Lye N T, Guanglan Z, Abeyratne U R, Saratchandran P. RBF    networks for source localization in quantitative electrophysiology.    EMBS. 1998. pp. 2190-2192. [October 29 November 1, Hong Kong]-   Abeyratne R, Kinouchi Y, Oki H, Okada J, Shichjo F, Matsumoto K.    Artificial neural networks for source localization in the human    brain. Brain Topography. 1991; 4:321. doi: 10.1007/BF01129661.-   Abeyratne R, Zhang G, Saratchandran P. EEG source localization: a    comparative study of classical and neural network methods.    International Journal of Neural Systems. 2001; 11:349-360. doi:    10.1142/S0129065701000813.-   Kinouchi Y, Oki H, Okada J, Shichijo F, Matsumoto K. Artificial    neural networks for source localization in the human brain. Brain    Topography. 1991; 4:3-21. doi: 10.1007/BF01129661.-   Sclabassi R J, Sonmez M, Sun M. EEG source localization: a neural    network approach. Neurological Research. 2001; 23:457-464. doi:    10.1179/016164101101198848.-   Sun M, Sclabassi R J. The forward EEG solutions can be computed    using artificial neural networks. IEEE Transactions on Biomedical    Engineering. 2000; 47:1044-1050. doi: 10.1109/10.855931.-   Tun A K, Lye N T, Guanglan Z, Abeyratne U R, Saratchandran P. RBF    networks for source localization in quantitative electrophysiology.    Critical Reviews in Biomedical Engineering. 2000; 28:463-472.-   Van Hoey G, De Clercq J, Vanrumste B, Walle R Van de, Lemahieu I,    DHave M, Boon P. EEG dipole source localization using artificial    neural networks. Physics in Medicine and Biology. 2000; 45:997-1011.    doi: 10.1088/0031-9155/45/4/314.-   Yuasa M, Zhang Q, Nagashino H, Kinouchi Y. EEG source localization    for two dipoles by neural networks. Proceedings IEEE 20th Annual    International Conference IEEE/EMBS, October 29 November 1, Hong    Kong. 1998. pp. 2190-2192.-   Zhang Q, Yuasa M, Nagashino H, Kinoushi Y. Single dipole source    localization from conventional EEG using BP neural networks.    Proceedings IEEE 20th Annual International Conference IEEE/EMBS,    Oct. 29 Nov. 1. 1998. pp. 2163-2166.-   McNay D, Michielssen E, Rogers R L, Taylor S A, Akhtari M,    Sutherling W W. Multiple source localization using genetic    algorithms. Journal of Neuroscience Methods. 1996; 64:163-172. doi:    10.1016/0165-0270(95)00122-0.-   Weinstein D M, Zhukov L, Potts G. Localization of Multiple Deep    Epileptic Sources in a Realistic Head Model via Independent    Component Analysis. Tech. rep., School of Computing, University of    Utah; 2000.-   Zhukov L, Weinstein D, Johnson C R. Independent Component Analysis    for EEG Source Localization in Realistic Head Models. Proceedings of    the IEEE Engineering in Medicine and Biol. Soc., 22nd Annual    International Conference. 2000; 3:87-96.-   Salu Y, Cohen L G, Rose D, Sato S, Kufta C, Hallett M. An Improved    Method for Localizing Electric Brain Dipoles. IEEE Transactions on    Biomedical Engineering. 1990; 37:699-705. doi: 10.1109/10.55680.-   Yao J, Dewald J P A. Evaluation of different cortical source    localization methods using simulated and experimental EEG data.    NeuroImage. 2005; 25:369-382. doi: 10.1016j.neuroimage.2004.11.036.-   Cuffin B N. EEG Dipole Source Localization. IEEE Engineering in    Medicine and Biology. 1998; 17:118-122. doi: 10.1109/51.715495.-   Miltner W, Braun C, Johnson R, Jr, Simpson G, Ruchkin D. Atest of    brain electrical source analysis (BESA): a simulation study.    Electroenceph Clin Neurophysiol. 1994; 91:295-310. doi:    10.1016/0013-4694(94)90193-7.-   Ding L, He B. Spatio-Temporal EEG Source Localization Using a    Three-Dimensional Subspace FINE Approach in a Realistic Geometry    Inhomogeneous Head Model. IEEE Transactions on Biomedical    Engineering. 2006; 53:1732-1739. doi: 10.1109/TBME.2006.878118.-   Field A. Discovering statistics using SPSS: (and sex, drugs and rock    ‘n’ roll) 2. SAGE publications; 2005.-   Ochi A, Otsubo H, Chitoku S, Hunjan A, Sharma R, Rutka J T, Chuang S    H, Kamijo K, Yamazaki T, Snead O C. Dipole localization for    identification of neuronal generators in independent neighboring    interictal EEG spike foci. Epilepsia. 2001; 42:483-490. doi:    10.1046/j.1528-1157.2001.27000.x.-   Snead O C. Surgical treatment of medical refractory epilepsy in    childhood. Brain and Development 2001; 23:199-207. doi:    10.1016/S0387-7604(01)00204-2.-   Duchowny M, Jayakar P, Koh S. Selection criteria and preoperative    investigation of patients with focal epilepsy who lack a localized    structural lesion. Epileptic Disorders. 2000; 2:219-226.-   Harmony T, Femandez-Bouzas A, Marosi E, Fernandez T, Valdes P, Bosch    J, Riera J, Bemal J, Rodriguez M, Reyes A, Koh S. Frequency source    analysis in patients with brain lesions. Brain Topography. 1998;    8:109-117. doi: 10.1007/BF01199774.-   Isotani T, Tanaka H, Lehmann D, Pascual-Marqui R D, Kochi K, Saito    N, Yagyu T, Kinoshita T, Sasada K. Source localization of EEG    activity during hypnotically induced anxiety and relaxation. Int J    Psychophysiology. 2001; 41:143-153. doi:    10.1016/S0167-8760(00)00197-5.-   Dierks T, Stik W K, Maurer K. Electrical brain activity in    schizophrenia described by equivalent dipoles of FFT-data. Schizophr    Res. 1995; 14:145-154. doi: 10.1016/0920-9964(94)00032-4.-   Huang C, Wahlung L, Dierks T, Julin P, Winblad B, Jelic V.    Discrimination of Alzheimer's disease and mild cognitive impairment    by equivalent EEG sources: a cross-sectional and longitudinal study.    Clinical Neurophysiology. 2000; 111:1961-1967. doi:    10.1016/S1388-2457(00)00454-5.-   Lubar J F, Congedo M, Askew J H. Low-resolution electromagnetic    tomography (LORETA) of cerebral activity in chronic depressive    disorder. Int J Psychophysiol. 2003; 49:175-185. doi:    10.1016/S0167-8760(03)00115-6.-   Frei E, Gamma A, Pascual-Marqui R D, Lehmann D, Hell D, Vollenweider    F X. Localization of MD MA-induced brain activity in healthy    volunteers using low resolution brain electromagnetic tomography    (LORETA) Human Brain Mapping. 2001; 14:152-165. doi:    10.1002/hbm.1049.-   Michel C M, Pascual-Marqui R D, Stik W K, Koenig T, Lehmann D.    Frequency domain source localization shows state-dependent diazepam    effects in 47-channel EEG. J Neural Transm Gen Sect 1995;    99:157-171. doi: 10.1007/BF01271476.-   Boon P, D'Hav M, Vandekerckhove T, Achten E, Adam C, Clmenceau S,    Baulac M, Goosens L, Calliauw L, De Reuck J. Dipole modelling and    intracranial EEG recording: Correlation between dipole and ictal    onset zone. Acta Neurochir. 1997; 139:643-652. doi:    10.1007/BF01412000.-   Krings T, Chiappa K H, Cocchius J I, Connolly S, Cosgrove G R.    Accuracy of EEG dipole source localization using implanted sources    in the human brain. Clinical Neurophysiology. 1999; 110:106-114.    doi: 10.1016/S0013-4694(98)00106-0.-   Meret I. Dipole modeling of interictal and ictal EEG and MEG    paroxysms. Epileptic Disord. 2001; 3:11-36. [(special issue)]-   Paetau R, Granstrom M, Blomstedt G, Jousmaki V, Korkman M.    Magnetoencephalography in presurgical evaluation of children with    Landau-Kleffner syndrome. Epilepsia. 1999; 40:326-335. doi:    10.1111I.1528-1157.1999.tb00713.x.-   Roche-Labarbe N, Aarabi A, Kongolo G, Gondry-Jouet C, Dmpelmann M,    Grebe R, Wallois F. High-resolution electroencephalography and    source localization in neonates. Human Brain Mapping. 2007. p. 40.-   John E R, Prichep L S, Valdes-Sosa P, Bosch J, Aubert E, Gugino L D,    Kox W, Tom M, Di Michele F. Invariant reversible QEEG effects of    anesthetics. Consciousness and Cognition. 2001; 10:165-183. doi:    10.1006/ccog.2001.0507.-   Lantz G, Grave de Peralta R, Gonzalez S, Michel C M. Noninvasive    localization of electromagnetic epileptic activity. II.    Demonstration of sublobar accuracy in patients with simultaneous    surface and depth recordings. Brain Topography. 2001; 14:139-147.    doi: 10.1023/A: 1012996930489.-   Meret I, Gotman J. Dipole modeling of scalp electroencephalogram    epileptic discharges: correlation with intracerebral fields.    Clinical Neurophysiolology. 2001; 112:414-430. doi:    10.1016/S1388-2457(01)00458-8.

The first applications of blind decomposition to biomedical time seriesanalysis applied the infomax independent component analysis (ICA)algorithm to decomposition of EEG and event-related potential (ERP) dataand reported the use of ICA to monitor alertness. This separatedartifacts, and EEG data into constituent components defined by spatialstability and temporal independence. ICA can also be used to removeartifacts from continuous or event-related (single-trial) EEG data priorto averaging. Vigario et al. (1997), using a different ICA algorithm,supported the use of ICA for identifying artifacts in MEG data.Meanwhile, widespread interest in ICA has led to multiple applicationsto biomedical data as well as to other fields (Jung et al., 2000b). Mostrelevant to EEG/MEG analysis, ICA is effective in separatingfunctionally independent components of functional magnetic resonanceimaging (fMRI) data

Since the publication of the original infomax ICA algorithm, severalextensions have been proposed. Incorporation of a ‘natural gradient’term avoided matrix inversions, greatly speeding the convergence of thealgorithm and making i practical for use with personal computers onlarge data EEG and fMRI data sets. An initial ‘sphering’ step furtherincreased the reliability of convergence of the algorithm. The originalalgorithm assumed that sources have ‘sparse’ (super-Gaussian)distributions of activation values. This restriction has recently beenrelaxed in an ‘extended-ICA’ algorithm that allows both super-Gaussianand sub-Gaussian sources to be identified. A number of variant ICAalgorithms have appeared in the signal processing literature. Ingeneral, these make more specific assumptions about the temporal orspatial structure of the components to be separated, and typically aremore computationally intensive than the infomax algorithm.

Since individual electrodes (or magnetic sensors) each record a mixtureof brain and non-brain sources, spectral measures are difficult tointerpret and compare across scalp channels. For example, an increase incoherence between two electrode signals may reflect the activation of astrong brain source projecting to both electrodes, or the deactivationof a brain generator projecting mainly to one of the electrodes. Ifindependent components of the EEG (or MEG) data can be considered tomeasure activity within functionally distinct brain networks, however,event-related coherence between independent components may revealtransient, event-related changes in their coupling and decoupling (atone or more EEG/MEG frequencies). ERCOH analysis has been applied toindependent EEG components in a selective attention task.

Relational Database A database management system (DBMS) is the softwarewhich controls the storage, retrieval, deletion, security, and integrityof data within a database. A relational database management system(BDBMS) stores data in tables. Tables are organized into columns, andeach column stores one type of data (integer, real number, characterstrings, date, . . . ). The data for a single “instance” of a table isstored as a row. For example, an emotional neural correlate table wouldhave columns such as EmotionLabel, NeuralCorrelate1_under_condition1,NeuralCorrelate2_under_condition2, NeuralCorrelate3_under_condition3,NeuralCorrelate4_under_condition4, etc. Tables typically have keys, oneor more columns that uniquely identify a row within the table, in thecase of the Emlational neural correlate table the key would beEmotionLabel. To improve access time to a data table an index on thetable is defined. An index provides a quick way to look up data based onone or more columns in the table. The most common use of RDBMSs is toimplement simple Create, Read, Update, and Delete. A relational databasemay be manipulated using Structured Query Language (SQL) statements.en.wikipedia.org/wiki/Relational_database. The relational database maybe a SQL or noSQL database.

SUMMARY OF THE INVENTION

In other embodiments, the processing of the brain activity patterns doesnot seek to classify or characterize it, but rather to filter andtransform the information to a form suitable for control of thestimulation of the second subject In particular, according to thisembodiment the subtleties that are not yet reliably classified intraditional brain activity pattern analysis are respected. For example,it is understood that all brain activity is reflected in synapticcurrents and other neural modulation and, therefore, theoretically,conscious and subconscious information is, in theory, accessible throughbrain activity pattern analysis. Since the available processingtechnology generally fails to distinguish a large number of differentbrain activity patterns, that available processing technology, isnecessarily deficient, but improving. However, just because acomputational algorithm is unavailable to extract the information, doesnot mean that the information is absent Therefore, this embodimentemploys relatively raw brain activity pattern data, such as filtered orunfiltered EEGs, to control the stimulation of the second subject,without a full comprehension or understanding of exactly whatinformation of significance is present In one embodiment, brainwaves arerecorded and “played back” to another subject, similar to recoding andplaying back music. Such recording-playback may be digital or analog.Typically, the stimulation may include a low dimensionality stimulus,such as stereo-optic, binaural, isotonic tones, tactile, or othersensory stimulation, operating bilaterally, and with control overfrequency and phase and/or waveform and/or transcranial stimulation suchas TES, tDCS, HD-tDCS, tACS, or TMS. A plurality of different types ofstimulation may be applied concurrently, e.g., visual, auditory, othersensory, magnetic, electrical.

Likewise, a present lack of understanding of the essentialcharacteristics of the signal components in the brain activity patternsdoes not prevent their acquisition, storage, communication, andprocessing (to some extent). The stimulation may be direct, i.e., avisual, auditory, or tactile stimulus corresponding to the brainactivity pattern, or a derivative or feedback control based on thesecond subjects brain activity pattern.

To address the foregoing problems, in whole or in part, and/or otherproblems that may have been observed by persons skilled in the art, thepresent disclosure provides methods, processes, systems, apparatus,instruments, and/or devices, as described by way of example inimplementations set forth below.

While mental states are typically considered internal to the individual,and subjective, in fact, such states are common across individuals andhave determinable physiological and electrophysiological populationcharacteristics. Further, mental states may be externally changed orinduced in a manner that bypasses the normal cognitive processes. Insome cases, the triggers for the mental state are subjective, andtherefore the particular subject-dependent sensory or excitation schemerequired to induce a particular state will differ. For example,olfactory stimulation can have different effects on different people,based on differences in history of exposure, social and cultural norms,and the like. On the other hand, some mental state response triggers arenormative, for example “tearjerker” media.

Mental states are represented in brainwave patterns, and in normalhumans, the brainwave patterns and metabolic (e.g. blood flow, oxygenconsumption, etc.) follow prototypical patterns. Therefore, bymonitoring brainwave patterns in an individual, a state or series ofmental states in that person may be determined or estimated. However,the brainwave patterns may be interrelated with context, other activity,and past history. Further, while prototypical patterns may be observed,there are also individual variations in the patterns. The brainwavepatterns may include characteristic spatial and temporal patternsindicative of mental state. The brainwave signals of a person may beprocessed to extract these patterns, which, for example, may berepresented as hemispheric signals within a frequency range of 3-100 Hz.These signals may then be synthesized or modulated into one or morestimulation signals, which are then employed to induce a correspondingmental state into a recipient, in a manner seeking to achieve a similarbrainwave pattern from the source. The brainwave pattern to beintroduced need not be newly acquired for each case. Rather, signals maybe acquired from one or more individuals, to obtain an exemplar forvarious respective mental state. Once determined, the processed signalrepresentation may be stored in a non-volatile memory for later use.However, in cases of complex interaction between a mental state and acontext or content or activity, i may be appropriate to derived thesignals from a single individual whose context or content-environment oractivity is appropriate for the circumstances. Further, in some cases, asingle mental state, emotion or mood is not described or fullycharacterized, and therefore acquiring signals from a source is anefficient exercise.

With a library of target brainwave patterns, a system and method isprovided in which a target subject may be immersed in a presentation,which includes not only multimedia content, but also a series of definedmental states, emotional states or moods that accompany the multimediacontent In this way, the multimedia presentation becomes fullyimmersive. The stimulus in this case may be provided through a headset,such as a virtual reality or augmented reality headset. This headset isprovided with a stereoscopic display, binaural audio, and a set of EEGand transcranial stimulatory electrodes. These electrodes (i provided)typically deliver a subthreshold signal, which is not painful, which istypically an AC signal which corresponds to the desired frequency,phase, and spatial location of the desired target pattern. Theelectrodes may also be used to counteract undesired signals, bydestructively interfering with them while concurrently imposing thedesired patterns. The headset may also generate visual and/or auditorysignals which correspond to the desired state. For example, the auditorysignals may induce binaural beats, which cause brainwave entrainment.The visual signals may include intensity fluctuations or othermodulation patterns, especially those which are subliminal, that arealso adapted to cause brainwave entrainment or induction of a desiredbrainwave pattern.

The headset preferably includes EEG electrodes for receiving feedbackfrom the user. That is, the stimulatory system seeks to achieve a mentalstate, emotion or mood response from the user. The EEG electrodes permitdetermination of whether that state is achieved, and if not, what thecurrent state is. It may be that achieving a desired brainwave patternis state dependent, and therefore that characteristics of the stimulusto achieve a desired state depend on the starting state of the subjectOther ways of determining mental state, emotion, or mood includeanalysis of facial expression, electromyography (EMG) analysis of facialmuscles, explicit user feedback, etc.

An authoring system is provided which permits a content designer todetermine what mental states are desired, and then encode those statesinto media, which is then interpreted by a media reproduction system inorder to generate appropriate stimuli. As noted above, the stimuli maybe audio, visual, multimedia, other senses, or electrical or magneticbrain stimulation, and therefore a VR headset with transcranialelectrical or magnetic stimulation is not required. Further, in someembodiments, the patterns may be directly encoded into the audiovisualcontent, subliminally encoded.

In some cases, the target mental state may be derived from an expert,actor or professional exemplar. The states may be read based on facialexpressions, EMG, EEG, or other means, from the actor or exemplar. Forexample, a prototype exemplar engages in an activity that triggers aresponse, such as viewing the Grand Canyon or artworks within theLouvre. The responses of the exemplar are then recorded or represented,and preferably brainwave patterns recorded that represent the responses.A representation of the same experience is then presented to the target,with a goal of the target also experiencing the same experience as theexemplar. This is typically a voluntary and disclosed process, so thetarget will seek to willingly comply with the desired experiences. Insome cases, the use of the technology is not disclosed to the target,for example in advertising presentations or billboards. In order for anactor to serve as the exemplar, the emotions achieved by that personmust be authentic. However, so-called “method actors” do authenticallyachieve the emotions they convey. However, in some cases, for examplewhere facial expressions are used as the indicator of mental state, anactor can present desired facial expressions with inauthentic mentalstates. The act of making a face corresponding to an emotion oftenachieves the targeted mental state.

In order to calibrate the system, the brain pattern of a person may bemeasured while in the desired state. The brain patterns acquired forcalibration or feedback need not be of the same quality, or precision,or data depth, and indeed may represent responses rather than primaryindicia. That is, there may be some asymmetry in the system, between thebrainwave patterns representative of a mental state, and the stimuluspatterns appropriate for inducing the brain state.

The present invention generally relates to achieving a mental state in asubject by conveying to the brain of the subject patterns of brainwaves.These brainwaves may be artificial or synthetic, or derived from thebrain of a second subject (e.g., a person experiencing an authenticexperience or engaged in an activity). Typically, the wave patterns ofthe second subject are derived while the second subject is experiencingan authentic experience.

A special case is where the first and second subjects are the sameindividual. For example, brainwave patterns are recorded while a subjectis in a particular mental state. That same pattern may assist inachieving the same mental state at another time. Thus, there may be atime delay between acquisition of the brainwave information from thesecond subject, and exposing the first subject to correspondingstimulation. The signals may be recorded and transmitted.

The temporal pattern may be conveyed or induced non-invasively via light(visible or infrared), sound (or ultrasound), transcranial direct oralternating current stimulation (tDCS or tACS), transcranial magneticstimulation (TMS), Deep transcranial magnetic stimulation (Deep TMS, ordTMS), Repetitive Transcranial Magnetic Stimulation (rTMS) olfactorystimulation, tactile stimulation, or any other means capable ofconveying frequency patterns. In a preferred embodiment, normal humansenses are employed to stimulate the subject, such as light, sound,smell and touch. Combinations of stimuli may be employed. In some cases,the stimulus or combination is innate, and therefore largelypan-subject. In other cases, response to a context is learned, andtherefore subject-specific. Therefore, feedback from the subject may beappropriate to determine the triggers and stimuli appropriate to achievea mental state.

This technology may be advantageously used to enhance mental response toa stimulus or context Still another aspect provides for a change in themental state. The technology may be used in humans or animals.

The present technology may employ an event-correlated EEG time and/orfrequency analysis performed on neuronal activity patterns. In atime-analysis, the signal is analyzed temporally and spatially,generally looking for changes with respect to time and space. In afrequency analysis, over an epoch of analysis, the data, which istypically a time-sequence of samples, is transformed, using e.g., aFourier transform (FT, or one implementation, the Fast FourierTransform, FFT), into a frequency domain representation, and thefrequencies present during the epoch are analyzed. The window ofanalysis may be rolling, and so the frequency analysis may becontinuous. In a hybrid time-frequency analysis, for example, a waveletanalysis, the data during the epoch is transformed using a “Wavelettransform”, e.g., the Discrete Wavelet Transform (DWT) or continuouswavelet transform (CWT), which has the ability to construct atime-frequency representation of a signal that offers very good time andfrequency localization. Changes in transformed data over time and spacemay be analyzed. In general, the spatial aspect of the brainwaveanalysis is anatomically modelled. In most cases, anatomy is considereduniversal, but in some cases, there are significant differences. Forexample, brain injury, psychiatric disease, age, race, native language,training, sex, handedness, and other factors may lead to distinctspatial arrangement of brain function, and therefore when transferringmood from one individual to another, it is preferred to normalize thebrain anatomy of both individuals by experiencing roughly the sameexperiences, and measuring spatial parameters of the EEG or MEG. Notethat spatial organization of the brain is highly persistent, absentinjury or disease, and therefore this need only be performedinfrequently. However, since electrode placement may be inexact, aspatial calibration may be performed after electrode placement.

Different aspects of EEG magnitude and phase relationships may becaptured, to reveal details of the neuronal activity. The“time-frequency analysis” reveals the brain's parallel processing ofinformation, with oscillations at various frequencies within variousregions of the brain reflecting multiple neural processes co-occurringand interacting. See, Lisman J, Buzsaki G. A neural coding scheme formedby the combined function of gamma and theta oscillations. SchizophrBull. Jun. 16, 2008; doi:10.1093/schbul/sbn060. Such a time-frequencyanalysis may take the form of a wavelet transform analysis. This may beused to assist in integrative and dynamically adaptive informationprocessing. Of course, the transform may be essentially lossless and maybe performed in any convenient information domain representation. TheseEEG-based data analyses reveal the frequency-specific neuronaloscillations and their synchronization in brain functions ranging fromsensory processing to higher-order cognition. Therefore, these patternsmay be selectively analyzed, for transfer to or induction in, a subject.

A statistical clustering analysis may be performed in high dimensionspace to isolate or segment regions which act as signal sources, and tocharacterize the coupling between various regions. This analysis mayalso be used to establish signal types within each brain region, anddecision boundaries characterizing transitions between different signaltypes. These transitions may be state dependent, and therefore thetransitions may be detected based on a temporal analysis, rather thanmerely a concurrent oscillator state.

The various measures make use of the magnitude and/or phase angleinformation derived from the complex data extracted from the EEG duringspectral decomposition and/or temporal/spatial/spectral analysis. Somemeasures estimate the magnitude or phase consistency of the EEG withinone channel across trials, whereas others estimate the consistency ofthe magnitude or phase differences between channels across trials.Beyond these two families of calculations, there are also measures thatexamine the coupling between frequencies, within trials and recordingsites. Of course, in the realm of time-frequency analysis, many types ofrelationships can be examined beyond those already mentioned.

These sensory processing specific neuronal oscillations, e.g., brainwavepatterns, e.g., of a subject (a “source”) or to a person trained (forexample, an actor trained in ‘the method’) to create a desired state,and can be stored on a tangible medium and/or can be simultaneouslyconveyed to a recipient making use of the brain's frequency followingresponse nature. See, Galbraith, Gary C., Darlene M. Olfman, and Todd M.Huffman. “Selective attention affects human brain stemfrequency-following response.” Neuroreport 14, no. 5 (2003): 735-738,journals.lww.com/neuroreport/Abstract/2003/04150/Selective_attention_affects_human_brain_stem.15.aspx.

Of course, in some cases, one or more components of the stimulation ofthe target subject (recipient) may be represented as abstract orsemantically defined signals, and, more generally, the processing of thesignals to define the stimulation will involve high level modulation ortransformation between the source signal received from the first subject(donor) or plurality of donors, to define the target signal forstimulation of the second subject (recipient).

Preferably, each component represents a subset of the neural correlatesreflecting brain activity that have a high autocorrelation in space andtime, or in a hybrid representation such as wavelet. These may beseparated by optimal filtering (e.g., spatial PCA), once thecharacteristics of the signal are known, and bearing in mind that thesignal is accompanied by a modulation pattern, and that the twocomponents themselves may have some weak coupling and interaction.

For example, if the first subject (donor) is listening to music, therewill be significant components of the neural correlates that aresynchronized with the particular music. On the other hand, the music perse may not be part of the desired stimulation of the target subject(recipient). Further, the target subject (recipient) may be in adifferent acoustic environment, and i may be appropriate to modify theresidual signal dependent on the acoustic environment of the recipient,so that the stimulation is appropriate for achieving the desired effect,and does not represent phantoms, distractions, or irrelevant orinappropriate content In order to perform signal processing, it isconvenient to store the signals or a partially processed representation,though a complete real-time signal processing chain may be implemented.

The stimulation may be one or more stimulus applied to the secondsubject (trainee or recipient), which may be an electrical or magnetictranscranial stimulation (tDCS, HD-tDCS, tACS, osc-tDCS, or TMS),sensory stimulation (e.g., visual, auditory, or tactile), mechanicalstimulation, ultrasonic stimulation, etc., and controlled with respectto waveform, frequency, phase, intensity/amplitude, duration, orcontrolled via feedback, self-reported effect by the second subject,manual classification by third parties, automated analysis of brainactivity, behavior, physiological parameters, etc. of the second subject(recipient).

Typically, the first and the second subjects are spatially remote fromeach other and may be temporally remote as well. In some cases, thefirst and second subject are the same subject (human or animal),temporally displaced. In other cases, the first and the second subjectare spatially proximate to each other. These different embodimentsdiffer principally in the transfer of the signal from at least one firstsubject (donor) to the second subject (recipient). However, when thefirst and the second subjects share a common environment, the signalprocessing of the neural correlates and, especially of real-timefeedback of neural correlates from the second subject, may involveinteractive algorithms with the neural correlates of the first subject.

According to another embodiment, the first and second subjects are eachsubject to stimulation. In one particularly interesting embodiment, thefirst subject and the second subject communicate with each other inreal-time, with the first subject receiving stimulation based on thesecond subject, and the second subject receiving feedback based on thefirst subject. This can lead to synchronization of neural correlates(e.g., neuronal oscillations, or brainwaves) and, consequently, ofemotional or mental state between the two subjects. The neuralcorrelates may be neuronal oscillations resulting in brainwaves that aredetectable as, for example, EEG, qEEG, or MEG signals. Traditionally,these signals are found to have dominant frequencies, which may bedetermined by various analyses, such as spectral analysis, waveletanalysis, or principal component analysis (PCA), for example. Oneembodiment provides that the modulation pattern of a brainwave of atleast one first subject (donor) is determined independent of thedominant frequency of the brainwave (though, typically, within the sameclass of brainwaves), and this modulation imposed on a brainwavecorresponding to the dominant frequency of the second subject(recipient). That is, once the second subject achieves that samebrainwave pattern as the first subject (which may be achieved by meansother than electromagnetic, mechanical, or sensory stimulation), themodulation pattern of the first subject is imposed as a way of guidingthe emotional or mental state of the second subject.

According to another embodiment, the second subject (recipient) isstimulated with a stimulation signal, which faithfully represents thefrequency composition of a defined component of the neural correlates ofat least one first subject (donor). The defined component may bedetermined based on a principal component analysis, independentcomponent analysis (ICI), eigenvector-based multivariable analysis,factor analysis, canonical correlation analysis (CCA), nonlineardimensionality reduction (NLDR), or related technique.

The stimulation may be performed, for example, by using a TES device,such as a tDCS device, a high-definition tDCS device, an osc-tDCSdevice, a pulse-tDCS (“electrosleep”) device, an osc-tDCS, a tACSdevice, a CES device, a TMS device, rTMS device, a deep TMS device, alight source, or a sound source configured to modulate the dominantfrequency on respectively the light signal or the sound signal. Thestimulus may be a light signal, a sonic signal (sound), an electricsignal, a magnetic field, olfactory or a tactile stimulation. Thecurrent signal may be a pulse signal or an oscillating signal. Thestimulus may be applied via a cranial electric stimulation (CES), atranscranial electric stimulation (TES), a deep electric stimulation, atranscranial magnetic stimulation (TMS), a deep magnetic stimulation, alight stimulation, a sound stimulation, a tactile stimulation, or anolfactory stimulation. An auditory stimulus may be, for example,binaural beats or isochronic tones.

The technology also provides a processor configured to process theneural correlates of emotional or mental state from the first subject(donor), and to produce or define a stimulation pattern for the secondsubject (recipient) selectively dependent on a waveform pattern of theneural correlates from the first subject. The processor may also performa PCA, a spatial PCA, an independent component analysis (ICA),eigenvalue decomposition, eigenvector-based multivariate analyses,factor analysis, an autoencoder neural network with a linear hiddenlayer, linear discriminant analysis, network component analysis,nonlinear dimensionality reduction (NLDR), or another statistical methodof data analysis.

A signal is presented to a second apparatus, configured to stimulate thesecond subject (recipient), which may be an open loop stimulationdependent on a non-feedback-controlled algorithm, or a closed loopfeedback dependent algorithm. The second apparatus produces astimulation intended to induce in the second subject (recipient) thedesired emotional or mental state).

A typically process performed on the neural correlates is a filtering toremove noise. In some embodiments, noise filters may be provided, forexample, at 50 Hz, 60 Hz, 100 Hz, 120 Hz, and additional overtones(e.g., tertiary and higher harmonics). The stimulator associated withthe second subject (recipient) would typically perform decoding,decompression, decryption, inverse transformation, modulation, etc.

Alternately, an authentic wave or hash thereof may be authenticated viaa blockchain, and thus authenticatable by an immutable record. In somecases, it is possible to use the stored encrypted signal in itsencrypted form, without decryption.

Due to different brain sizes, and other anatomical, morphological,and/or physiological differences, dominant frequencies associated withthe same emotional or mental state may be different in differentsubjects. Consequently, it may not be optimal to forcefully impose onthe recipient the frequency of the donor that may or may not preciselycorrespond to the recipients frequency associated with the sameemotional or mental state. Accordingly, in some embodiments, the donor'sfrequency may be used to start the process of inducing the desiredemotional or mental state in a recipient. As some point, when therecipient is close to achieving the desired emotional or mental state,the stimulation is either stopped or replaced with neurofeedbackallowing the brain of the recipient to find its own optimal frequencyassociated with the desired emotional or mental state.

In one embodiment, the feedback signal from the second subject may becorrespondingly encoded as per the source signal, and the error betweenthe two minimized. According to one embodiment, the processor mayperform a noise reduction distinct from a frequency-band filtering.According to one embodiment, the neural correlates are transformed intoa sparse matrix, and in the transform domain, components having a highprobability of representing noise are masked, while components having ahigh probability of representing signal are preserved. That is, in somecases, the components that represent modulation that are important maynot be known a priori. However, dependent on their effect in inducingthe desired response in the second subject (recipient), the ‘Important’components may be identified, and the remainder filtered or suppressed.The transformed signal may then be inverse-transformed and used as abasis for a stimulation signal.

According to another embodiment a method of emotional or mental statemodification, e.g., brain entrainment, is provided, comprising:ascertaining an emotional or mental state in a plurality of firstsubjects (donors); acquiring brainwaves of the plurality of firstsubjects (donors), e.g., using one of EEG and MEG, to create a datasetcontaining brainwaves corresponding to different emotional or mentalstates. The database may be encoded with a classification of emotionalor mental states, activities, environment, or stimulus patterns, appliedto the plurality of first subjects, and the database may includeacquired brainwaves across a large number of emotional or mental states,activities, environment or stimulus patterns, for example. In manycases, the database records will reflect a characteristic or dominatefrequency of the respective brainwaves.

The record(s) thus retrieved are used to define a stimulation patternfor the second subject (recipient). As a relatively trivial example, afemale recipient could be stimulated principally based on records fromfemale donors. Similarly, a child recipient of a certain age could bestimulated principally based on the records from children donors of asimilar age. Likewise, various demographic, personality, and/orphysiological parameters may be matched to ensure a high degree ofcorrespondence to between the source and target subjects. In the targetsubject, a guided or genetic algorithm may be employed to selectmodification parameters from the various components of the signal, whichbest achieve the desired target state based on feedback from the targetsubject.

Of course, a more nuanced approach is to process the entirety of thedatabase and stimulate the second subject based on a globalbrainwave-stimulus model, though this is not required, and also, theunderlying basis for the model may prove unreliable or inaccurate. Infact, i may be preferred to derive a stimulus waveform from only asingle first subject (donor), in order to preserve micro-modulationaspects of the signal, which, as discussed above, have not been fullycharacterized. However, the selection of the donor(s) need not be staticand can change frequency. The selection of donor records may be based onpopulation statistics of other users of the records, i.e., whether ornot the record had the expected effect, filtering donors whose responsepattern correlates highest with a given recipient, etc. The selection ofdonor records may also be based on feedback patterns from the recipient.

The process of stimulation typically seeks to target a desired emotionalor mental state in the recipient, which is automatically orsemi-automatically determined or manually entered. In one embodiment,the records are used to define a modulation waveform of a synthesizedcarrier or set of carriers, and the process may include a frequencydomain multiplexed multi-subcarrier signal (which is not necessarilyorthogonal). A plurality of stimuli may be applied concurrently, throughthe different subchannels and/or though different stimulator electrodes,electric current stimulators, magnetic field generators, mechanicalstimulators, sensory stimulators, etc. The stimulus may be applied toachieve brain entrainment (i.e., synchronization) of the second subject(recipient) with one or more first subjects (donors). If the pluralityof donors is mutually entrained, then each will have a correspondingbrainwave pattern dependent on the basis of brainwave entrainment. Thislink between donors may be helpful in determining compatibility betweena respective donor and the recipient. For example, characteristicpatterns in the entrained brainwaves may be determined, even fordifferent target emotional or mental states, and the characteristicpatterns may be correlated to find relatively close matches and toexclude relatively poor matches.

This technology may also provide a basis for a social network, datingsite, employment mission (e.g., space or military), or vocationaltesting, or other interpersonal environments, wherein people may bematched with each other based on entrainment characteristics. Forexample, people who efficiently entrain with each other may have bettercompatibility and, therefore, better marriage, work, or socialrelationships than those who do not. The entrainment effect need not belimited to emotional or mental states, and may arise across any context.

As discussed above, the plurality of first subjects (donors) may havetheir respective brainwave patterns stored in separate database records.Data from a plurality of first subjects (donors) is used to train theneural network, which is then accessed by inputting the target stageand/or feedback information, and which outputs a stimulation pattern orparameters for controlling a stimulator(s). When multiple first subject(donors) form the basis for the stimulation pattern, it is preferredthat the neural network output parameters of the stimulation, derivedfrom and comprising features of the brainwave patterns or other neuralcorrelates of the emotional or mental state from the plurality of firstsubject (donors), which are then used to control a stimulator which, forexample, generates its own carrier wave(s) which are then modulatedbased on the output of the neural network. A trained neural network neednot periodically retrieve records, and therefore may operate in a moretime-continuous manner, rather than the more segmented scheme ofrecord-based control.

In any of the feedback dependent methods, the brainwave patterns orother neural correlates of emotional or mental states may be processedby a neural network, to produce an output that guides or controls thestimulation. The stimulation is, for example, at least one of a lightsignal, a sound signal, an electric signal, a magnetic field, anolfactory signal, a chemical signal, and a vibration or mechanicalstimulus. The process may employ a relational database of emotional ormental states and brainwave patterns, e.g., frequencies/neural correlatewaveform patterns associated with the respective emotional or mentalstates. The relational database may comprise a first table, the firsttable further comprising a plurality of data records of brainwavepatterns, and a second table, the second table comprising a plurality ofemotional or mental states, each of the emotional or mental states beinglinked to at least one brainwave pattern. Data related to emotional ormental states and brainwave patterns associated with the emotional ormental states are stored in the relational database and maintained. Therelational database is accessed by receiving queries for selected(existing or desired) emotional or mental states, and data records arereturned representing the associated brainwave pattern. The brainwavepattern retrieved from the relational database may then be used formodulating a stimulator seeking to produce an effect selectivelydependent on the desired emotional or mental state.

A further aspect of the technology provides a computer apparatus forcreating and maintaining a relational database of emotional or mentalstates and frequencies associated with the emotional or mental state.The computer apparatus may comprise a non-volatile memory for storing arelational database of emotional or mental states and neural correlatesof brain activity associated with the emotional or mental states, thedatabase comprising a first table comprising a plurality of data recordsof neural correlates of brain activity associated with the emotional ormental states, and a second table comprising a plurality of emotional ormental states, each of the emotional or mental states being linked toone or more records in the first table; a processor coupled with thenon-volatile memory, and being configured to process relational databasequeries, which are then used for searching the database; RAM coupledwith the processor and the non-volatile memory for temporary holdingdatabase queries and data records retrieved from the relationaldatabase; and an IO interface configured to receive database queries anddeliver data records retrieved from the relational database. Astructured query language (SQL) or alternate to SQL (e.g., noSQL)database may also be used to store and retrieve records. A relationaldatabase described above maintained and operated by a general-purposecomputer, improves the operations of the general-purpose computer bymaking searches of specific emotional or mental states and brainwavesassociated therewith more efficient thereby, inter alia, reducing thedemand on computing power.

A further aspect of the technology provides a method of brainentrainment comprising: ascertaining an emotional or mental state in atleast one first subject (donor), recording brainwaves of said at leastone first subject (donor) using at least one channel of EEG and/or MEG;storing the recorded brainwaves in a physical memory device, retrievingthe brainwaves from the memory device, applying a stimulus signalcomprising a brainwave pattern derived from at least one-channel of theEEG and/or MEG to a second subject (recipient) via transcranialelectrical and/or magnetic stimulation, whereby the emotional or mentalstate desired by the second subject (recipient) is achieved. Thestimulation may be of the same dimension (number of channels) as the EEGor MEG, or a different number of channels, typically reduced. Forexample, the EEG or MEG may comprise 64, 128 or 256 channels, while thetranscranial stimulator may have 32 or fewer channels. The placement ofelectrodes used for transcranial stimulation may be approximately thesame as the placement of electrodes used in recording of EEG or MEG topreserve the topology of the recorded signals and, possibly, use thesesignals for spatial modulation.

One of the advantages of transforming the data is the ability to selecta transform that separates the information of interest represented inthe raw data, from noise or other information. Some transforms preservethe spatial and state transition history, and may be used for a moreglobal analysis. Another advantage of a transform is that it can presentthe information of interest in a form where relatively simple linear orstatistical functions of low order may be applied. In some cases, it isdesired to perform an inverse transform on the data. For example, if theraw data includes noise, such as 50 or 60 Hz interference, a frequencytransform may be performed, followed by a narrow band filtering of theinterference and its higher order intermodulation products. An inversetransform may be performed to return the data to its time-domainrepresentation for further processing. (In the case of simple filtering,a finite impulse response (FIR) or infinite impulse response (IIR)filter could be employed). In other cases, the analysis is continued inthe transformed domain.

Transforms may be part of an efficient algorithm to compress data forstorage or analysis, by making the representation of the information ofinterest consume fewer bits of information (if in digital form) and/orallow it to be communication using lower bandwidth. Typically,compression algorithms will not be lossless, and as a result, thecompression is irreversible with respect to truncated information.

Typically, the transformation(s) and filtering of the signal areconducted using traditional computer logic, according to definedalgorithms. The intermediate stages may be stored and analyzed. However,in some cases, neural networks or deep neural networks may be used,convolutional neural network architectures, or even analog signalprocessing. According to one set of embodiments, the transforms (if any)and analysis are implemented in a parallel processing environment Suchas using an SIMD processor such as a GPU (or GP GPU). Algorithmsimplemented in such systems are characterized by an avoidance ofdata-dependent branch instructions, with many threads concurrentlyexecuting the same instructions.

EEG signals are analyzed to determine the location (e.g., voxel or brainregion) from which an electrical activity pattern is emitted, and thewave pattern characterized. The spatial processing of the EEG signalswill typically precede the content analysis, since noise and artifactsmay be useful for spatial resolution. Further, the signal from one brainregion will typically be noise or interference in the signal analysisfrom another brain region; so the spatial analysis may represent part ofthe comprehension analysis. The spatial analysis is typically in theform of a geometrically and/or anatomically-constrained statisticalmodel, employing all of the raw inputs in parallel. For example, wherethe input data is transcutaneous electroencephalogram information, from32 EEG electrodes, the 32 input channels, sampled at e.g., 500 sps, 1ksps or 2 ksps, are processed in a four or higher dimensional matrix, topermit mapping of locations and communication of impulses over time,space and state.

The matrix processing may be performed in a standard computingenvironment, e.g., an i7-7920HQ, i7-8700K, or i9-7980XE processor, underthe Windows 10 operating system, executing MatLab (MathWorks, WoburnMass.) software platform. Alternately, the matrix processing may beperformed in a computer cluster or grid or cloud computing environment.The processing may also employ parallel processing, in either adistributed and loosely coupled environment, or asynchronous environmentOne preferred embodiment employs a single instruction, multiple dataprocessors, such as a graphics processing unit such as the nVidia CUDAenvironment or AMD Firepro high-performance computing environment.

Artificial intelligence (AI) and ML methods, such as artificial neuralnetworks, deep neural networks, etc., may be implemented to extract thesignals of interest Neural networks act as an optimized statisticalclassifier and may have arbitrary complexity. A so-called deep neuralnetwork having multiple hidden layers may be employed. The processing istypically dependent on labeled training data, such as EEG data, orvarious processed, transformed, or classified representations of the EEGdata. The label represents the emotion, mood, context, or state of thesubject during acquisition. In order to handle the continuous stream ofdata represented by the EEG, a recurrent neural network architecture maybe implemented. Depending preprocessing before the neural network,formal implementations of recurrence may be avoided. A four or moredimensional data matrix may be derived from the traditionalspatial-temporal processing of the EEG and fed to a neural network.Since the time parameter is represented in the input data, a neuralnetwork temporal memory is not required, though this architecture mayrequire a larger number of inputs. Principal component analysis (PCA,en.wikipedia.org/wiki/Principal_component_analysis), spatial PCA(arxiv.org/pdf/1501.03221v3.pdf,adegenet.r-forge.r-project.org/flles/tutorial-spca.pdf,www.ncbi.nlm.nih.gov/pubmed/1510870); and clustering analysis may alsobe employed (en.wikipedia.org/wiki/Cluster_analysis, see U.S. Pat. Nos.9,336,302, 9,607,023 and cited references).

In general, a neural network of this type of implementation will, inoperation, be able to receive unlabeled EEG data, and produce the outputsignals representative of the predicted or estimated task, performance,context, or state of the subject during acquisition of the unclassifiedEEG. Of course, statistical classifiers may be used rather than neuralnetworks.

The analyzed EEG, either by conventional processing, neural networkprocessing, or both, serves two purposes. First, it permits one todeduce which areas of the brain are subject to which kinds of electricalactivity under which conditions. Second, it permits feedback duringtraining of a trainee (assuming proper spatial and anatomical correlatesbetween the trainer and trainee), to help the system achieve the desiredstate, or as may be appropriate, desired series of states and/or statetransitions. According to one aspect of the technology, the appliedstimulation is dependent on a measured starting state or status (whichmay represent a complex context and history dependent matrix ofparameters), and therefore the target represents a desired complexvector change. Therefore, this aspect of the technology seeks tounderstand a complex time-space-brain activity associated with anactivity or task in a trainer, and to seek a corresponding complextime-space-brain activity associated with the same activity or task in atrainee, such that the complex time-space-brain activity state in thetrainer is distinct from the corresponding state sought to be achievedin the trainee. This permits transfer of training paradigms fromqualitatively different persons, in different contexts, and, to someextent, to achieve a different result.

The conditions of data acquisition from the trainer will include bothtask data, and sensory-stimulation data. That is, a preferredapplication of the system is to acquire EEG data from a trainer orskilled individual, which will then be used to transfer learning, ormore likely, learning readiness states, to a naïve trainee. The goal forthe trainee is to produce a set of stimulation parameters that willachieve, in the trainee, the corresponding neural activity resulting inthe EEG state of the trainer at the time of or preceding the learning ofa skill or a task, or performance of the task.

It is noted that EEG is not the only neural or brain activity or statedata that may be acquired, and of course any and all such data may beincluded within the scope of the technology, and therefore EEG is arepresentative example only of the types of data that may be used. Othertypes include fMRI, magnetoencephalogram, motor neuron activity, PET,etc.

While mapping the stimulus-response patterns distinct from the task isnot required in the trainer, it is advantageous to do so, because thetrainer may be available for an extended period, the stimulus of thetrainee may influence the neural activity patterns, and it is likelythat the trainer will have correlated stimulus-response neural activitypatterns with the trainee(s). It should be noted that the foregoing hassuggested that the trainer is a single individual, while in practice,the trainer may be a population of trainers or skilled individuals. Theanalysis and processing of brain activity data may, therefore, beadaptive, both for each respective individual and for the population asa whole.

For example, the system may determine that not all human subjects havecommon stimulus-response brain activity correlates, and therefore thatthe population needs to be segregated and clustered. If the differencesmay be normalized, then a normalization matrix or other correction maybe employed. On the other hand, if the differences do not permitfeasible normalization, the population(s) may be segmented, withdifferent trainers for the different segments. For example, in sometasks, male brains have different activity patterns and capabilitiesthan female brains. This, coupled with anatomical differences betweenthe sexes, implies that the system may provide gender-specificimplementations. Similarly, age differences may provide a rational andscientific basis for segmentation of the population. However, dependingon the size of the information base and matrices required, and someother factors, each system may be provided with substantially allparameters required for the whole population, with a user-specificimplementation based on a user profile or initial setup, calibration,and system training session.

According to one aspect of the present invention, a source subject isinstrumented with sensors to determine localized brain activity duringexperiencing an event. The objective is to identify regions of the braininvolved in processing this response.

The sensors will typically seek to determine neuron firing patterns andbrain region excitation patterns, which can be detected by implantedelectrodes, transcutaneous electroencephalograms, magnetoencephalograms,fMRI, and other technologies. Where appropriate, transcutaneous EEG ispreferred, since this is non-invasive and relatively simple.

The source is observed with the sensors in a quiet state, a state inwhich he or she is experiencing an event, and various control states inwhich the source is at rest or engaged in different activities resultingin different states. The data may be obtained for a sufficiently longperiod of time and over repeated trials to determine the effect ofduration. The data may also be a population statistical result, and neednot be derived from only a single individual at a single time.

The sensor data is then processed using a 4D (or higher) model todetermine the characteristic location-dependent pattern of brainactivity over time associated with the state of interest Where the datais derived from a population with various degrees of arousal, the modelmaintains this arousal state variable dimension.

A recipient is then prepared for receipt of the mental state. The mentalstate of the recipient may be assessed. This can include responses to aquestionnaire, sell-assessment, or other psychological assessmentmethod. Further, the transcutaneous EEG (or other brain activity data)of the recipient may be obtained, to determine the starting state forthe recipient, as well as activity during experiencing the desiredmental state.

In addition, a set of stimuli, such as visual patterns, acousticpatterns, vestibular, smell, taste, touch (light touch, deep touch,proprioception, stretch, hot, cold, pain, pleasure, electricstimulation, acupuncture, etc.), vagus nerve (e.g., parasympathetic),are imposed on the subject, optionally over a range of baseline brainstates, to acquire data defining the effect of individual and variouscombinations of sensory stimulation on the brain state of the recipientPopulation data may also be used for this aspect.

The data from the source or population of sources (see above) may thenbe processed in conjunction with the recipient or population ofrecipient data, to extract information defining the optimal sensorystimulation over time of the recipient to achieve the desired brainstate resulting in the desired emotional or mental state.

In general, for populations of sources and recipients, the dataprocessing task is immense. However, the statistical analysis willgenerally be of a form that permits parallelization of mathematicaltransforms for processing the data, which can be efficiently implementedusing various parallel processors, a common form of which is a SIMD(single instruction, multiple data) processor, found in typical graphicsprocessors (GPUs). Because of the cost-efficiency of GPUs, it isreferred to implement the analysis using efficient parallelizablealgorithms, even if the computational complexity is nominally greaterthan a CISC-type processor implementation.

During emotional arousal of the recipient, the EEG pattern may bemonitored to determine if the desired state is achieved through thesensory stimulation. A closed loop feedback control system may beimplemented to modify the stimulation seeking to achieve the target. Anevolving genetic algorithm may be used to develop a user model, whichrelates the emotional or mental state, arousal and valence, sensorystimulation, and brain activity patterns, both to optimize the currentsession of stimulation and leaning, as well as to facilitate futuresessions, where the emotional or mental states of the recipient havefurther enhanced, and to permit use of the system for a range ofemotional or mental states.

The stimulus may comprise a chemical messenger or stimulus to alter thesubjects level of consciousness or otherwise alter brain chemistry orfunctioning. The chemical may comprise a hormone or endocrine analogmolecule, (such as adrenocorticotropic hormone (ACTH) (4-11)), astimulant (such as cocaine, caffeine, nicotine, phenethylamines), apsychoactive drug, psychotropic or hallucinogenic substance (a chemicalsubstance that alters brain function, resulting in temporary changes inperception, mood, consciousness and behavior such as pleasantness (e.g.euphoria) or advantageousness (e.g., increased alertness).

While typically, controlled or “illegal” substances are to be avoided,in some cases, these may be appropriate for use. For example, variousdrugs may alter the state of the brain to enhance or selectively enhancethe effect of the stimulation. Such drugs include stimulants (e.g.,cocaine, methylphenidate (Ritalin), ephedrine, phenylpropanolamine,amphetamines), narcotics/opiates (opium, morphine, heroin, methadone,oxymorphine, oxycodone, codeine, fentanyl), hallucinogens (lysergic adddiethylamide (LSD), PCP, MDMA (ecstasy), mescaline, psilocybin, magicmushroom (Psilocybe cubensis), Amanita muscaria mushroom,marijuana/cannabis), Salvia divinorum, diphenhydramine (Benadryl),flexeril, tobacco, nicotine, bupropion (Zyban), opiate antagonists,depressants, gamma aminobutyric acid (GABA) agonists or antagonists,NMDA receptor agonists or antagonists, depressants (e.g., alcohol,Xanax; Valium; Halcion; Librium; other benzodiazepines, Ativan;Klonopin; Amytal; Nembutal; Seconal; Phenobarbital, other barbiturates),psychedelics, disassociatives, and deliriants (e.g., a special class ofacetylcholine-inhibitor hallucinogen). For example, Carhart-Harrisshowed using fMRI that LSD and psilocybin caused synchronization ofdifferent parts of the brain that normally work separately by makingneurons fire simultaneously. This effect can be used to inducesynchronization of various regions of the brain to heighten theemotional state.

It is noted that a large number of substances, natural and artificial,can alter mood or arousal and, as a result, may impact emotions ornon-target mental states. Typically, such substances will cross theblood-brain barrier, and exert a psychotropic effect. Often, however,this may not be necessary or appropriate. For example, a painfulstimulus can alter mood, without acting as a psychotropic drug; on theother hand, a narcotic can also alter mood by dulling emotions. Further,sensory stimulation can induce mood and/or emotional changes, such assmells, sights, sounds, various types of touch and proprioceptionsensation, balance and vestibular stimulation, etc. Therefore,peripherally acting substances that alter sensory perception orstimulation may be relevant to mood. Likewise, pharmacopsychotropicdrugs may alter alertness, perceptiveness, memory, and attention, whichmay be relevant to task-specific mental state control.

It is an object to provide a method for inducing an emotional state in asubject, comprising: determining a desired emotional state; selecting aprofile from a plurality of profiles stored in a memory, the pluralityof profiles each corresponding to a brain activity pattern of at leastone exemplar subject under a respective emotional state (the “source”);and exposing a target subject (the “recipient”) to a stimulus modulatedaccording to the selected profile, wherein the exposure, stimulus, andmodulation are adapted to induce, in the target subject the desiredemotional state.

The brain activity pattern may be an electroencephalographic brainwavepattern, a magnetoencephalographic brainwave pattern, an electricalbrainwave pattern, or a metabolic rate pattern, for example.

The stimulus comprises may visual stimulus, an auditory stimulus; anolfactory stimulus; a tactile stimulus; a proprioceptive stimulus; anelectrical stimulus; or a magnetic stimulus.

The desired emotional state is may be happiness, joy, gladness,cheerfulness, bliss, delight, ecstasy, optimism, exuberance, merriment,joviality; vivaciousness, pleasure, excitement, sexual arousal,relaxation, harmony, or peace, for example.

The exemplar subject and the target subject may be the same human atdifferent times, or different humans, or different species.

The stimulus may comprise an auditory stimulus adapted to inducebinaural beats.

The stimulus may comprise a dynamically changing electromagnetic fieldadapted synchronize brainwave patterns corresponding to the brainactivity pattern of at least one exemplar subject under the desiredemotional state.

The selected profile may be derived from measurements of brainwavepatterns in the exemplar subject selectively acquired during the desiredemotional state.

The selected profile may comprise a model derived from at least spatial,frequency and phase analysis of the measured brainwave patterns.

The stimulus may comprise an auditory or visual stimulus frequencycorresponding to a frequency pattern in a brainwave pattern of theexemplar subject.

The target subject may be concurrently exposed to the stimulus and aprimary audio or visual presentation which does not induce the desiredemotional state, wherein the stimulus does not substantially interferewith the target subject appreciation of the audio or visualpresentation.

The method may further comprise recording EEG signals of the exemplarsubject in the desired emotional state; decoding at least one of atemporal and a spatial pattern from the recorded EEG signals; andstoring the decoded at least one of temporal and spatial pattern in anon-volatile memory.

The method may further comprise selectively modifying the pattern basedon differences between the exemplar subject and the target subject.

The stimulus may comprise applying a spatial electrical stimulationpattern to the target subject via transcranial electrical stimulation(tES) to induce the desired emotional state. The spatial electricalstimulation pattern comprises a direct current or an alternatingcurrent. The transcranial electrical stimulation (tES) may be at leastone of a transcranial direct current stimulation (tDCS), a transcranialalternating current stimulation (tACS), a transcranial pulsed currentstimulation (tPCS) transcranial pulsed current stimulation (tPCS), and atranscranial random noise stimulation (tRNS).

The brain activity pattern of the at least one exemplar subject maycomprise a magnetoencephalogram (MEG), and the stimulus comprisesapplying a spatial magnetic stimulation pattern to the target subjectvia transcranial magnetic stimulation (tMS) to induce the desiredemotional state.

The stimulus may achieve brain entrainment in the target subject.

The method may further comprise determining a second desired emotionalstate; selecting a second profile from the plurality of profiles storedin a memory; and exposing a target subject to a stimulus modulatedaccording to the selected second profile, wherein the exposure,stimulus, and modulation are adapted to induce, in the target subjectthe desired second emotional state, the second emotional state beingdifferent from the first subsequent state and being induced insuccession after the emotional state.

It is another object to provide a method of brainwave entrainmentcomprising the steps of: recording EEG of the brainwaves of a firstsubject in an emotional state; decoding at least one of a temporal and aspatial pattern from the EEG; storing a representation of the pattern ina non-volatile memory; retrieving said pattern from the non-volatilememory modulating the temporal and spatial patterns on a stimulussignal; and applying the stimulus signal to a second subject. Thestimulus signal may be an alternating current, and said applyingcomprises applying the alternating current to the second subject viatranscranial alternating current stimulation (tACS) to induce theemotional state.

It is a further object to provide a method of brainwave entrainmentcomprising the steps of: recording EEG of the brainwaves of a firstsubject in a respective emotional state; decoding at least one oftemporal and spatial pattern from the recorded EEG; storing said atleast one of temporal and spatial pattern in a non-volatile memory;retrieving said at least one of temporal and spatial pattern from thenon-volatile memory; modulating the temporal and spatial patters on alight signal; and projecting the light signal to the second subject toinduce the respective emotional state. The light signal may be selectedfrom the group consisting of an ambient light signal, a directionallight signal, a laser beam signal, a visible spectrum light signal andan infrared light signal.

It is another object to provide a method of brainwave entrainmentcomprising the steps of: recording EEG of the brainwaves of a firstsubject in an emotional state; decoding at least one of a temporal and aspatial pattern from the EEG; storing said at least one of the temporaland the spatial pattern in a non-volatile memory; retrieving the atleast one of the temporal and the spatial pattern from the non-volatilememory; modulating the temporal and spatial patters on an isotonic soundsignal; and projecting the isotonic sound signal to a second subject toinduce the emotional state.

A still further object provides a method of brainwave entrainmentcomprising the steps of: recording EEG of the brainwaves of a firstsubject in an emotional state; decoding temporal frequency pattern fromthe EEG; storing the decoded temporal frequency pattern in a memory;retrieving the temporal frequency pattern from the memory; computing afirst set of frequencies by adding a predetermined delta to thefrequencies of the temporal frequency pattern; computing a second set offrequencies by subtracted the delta from the frequencies of the temporalfrequency pattern; modulating the first set of frequencies on a firstacoustical signal; modulating the second set of frequencies on a secondacoustical signal; projecting the first set of frequencies into a firstear of the second subject; and projecting the second set of frequenciesinto a second ear of the second subject, thereby producing binauralstimulation to induce the emotional state.

Another object provides a method for modifying an emotional state ormood in a subject, comprising: selecting an emotional state or moodprofile from a memory, corresponding to a brain activity pattern of atleast one exemplar subject in a respective emotional state or mood; andexposing a target subject to a stimulus signal modulated according tothe selected emotional state or mood profile, to induce, in the targetsubject the selected emotional state or mood. The brain activity patternmay be acquired through at least one of an electroencephalogram (EEG)and a magnetoencephalogram (EEG). The stimulus signal may be selectedfrom the group consisting of a light, a sound, a touch, a smell, anelectric current, and a magnetic field. The emotional state or mood maybe selected from the group consisting of a state of happiness, a stateof joy, a state of gladness, a state of cheerfulness, a state of bliss,a state of delight, a state of ecstasy, a state of optimism, a state ofexuberance, a state of merriment, a jovial state, a state ofvivaciousness, a state of pleasure, a state of excitement, a state ofrelaxation, a state of harmony, and a state of peace. The exemplarsubject and the target subject may be the same subject at differenttimes or different subjects.

A further object provides a method of brainwave entrainment comprisingthe steps of: recording EEG of a first subject in a positive emotionalstate; storing a spatial-temporal pattern corresponding to the EEG in amemory; modulating a stimulus pattern according to the spatial-temporalpattern; and stimulating a second subject with the modulated stimuluspattern, to induce the positive emotional state. The modulated stimuluspattern may comprise a binaural audio stimulus. The modulated stimuluspattern may comprise a transcranial electrical stimulation, e.g., adirect current stimulus, an alternating current stimulus, a transcranialdirect current stimulation (tDCS), a transcranial alternating currentstimulation (tACS), a transcranial pulsed current stimulation (tPCS)transcranial pulsed current stimulation (tPCS), or a transcranial randomnoise stimulation (tRNS).

It is a still further object to provide a method of brainwaveentrainment comprising the steps of: modulating a predefined temporaland spatial pattern on a magnetic field; and applying the modulatedmagnetic field to the brain of a subject via transcranial magneticstimulation (tMS) to selectively induce an emotional state correspondingto the predefined temporal and spatial pattern.

It is an object to provide a system and method for enhancing emotionalresponse to a stimulus in a subject.

It is another object to provide a system and method for enhancing theexperience virtual reality by enhancing the emotional response in asubject.

It is a further object to provide a system and method for enhancingcinematographic experience by enhancing the emotional response inviewers while watching a movie.

It is yet another object to provide a system and method for improvingusers' interaction with a computer.

It is still another object to provide a system and method for improvingusers' interaction with a robot.

It is a further object to provide a system and method for acceleratingmemory-retention and recall by inducing a desired emotional state in asubject.

It is yet another object to provide a system and method for treatment ofpatients with dementia.

It is an object to provide a system and method for facilitating anemotional state achievement process, compromising: determining aneuronal activity pattern, of a subject while engaged in a respectiveemotion; processing the determined neuronal activity pattern with atleast one automated processor and subjecting a subject seeking toachieve the respective emotion to a stimulus selected from the groupconsisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the processed electromagnetic determined neuronal activitypattern.

It is yet another object to provide a system and method for facilitatinga mental process, compromising: determining a neuronal activity patternof a skilled subject having the mental process; processing thedetermined neuronal activity pattern with at least one automatedprocessor and subjecting a subject seeking a corresponding mentalprocess to a stimulus selected from the group consisting of one or moreof a sensory excitation, a peripheral excitation, a transcranialexcitation, and a deep brain stimulation, dependent on the processedelectromagnetic determined neuronal activity pattern.

It is still another object to provide a system and method for improvingachieving a mental state, compromising: determining a neuronal activitypattern, of a subject while having the mental state; processing thedetermined neuronal activity pattern with at least one automatedprocessor and subjecting a subject seeking to achieve the mental stateto a stimulus selected from the group consisting of one or more of asensory excitation, a peripheral excitation, a transcranial excitation,and a deep brain stimulation, dependent on the processed electromagneticdetermined neuronal activity pattern. The mental state is, e.g., anemotional state, a mood, or other subjective state.

It is also an object to provide an apparatus for facilitating controlover an emotional state, compromising: an input, configured to receivedata representing a neuronal activity pattern of a subject while havingan emotional state; at least one automated processor, configured toprocess the determined neuronal activity pattern, to determine neuronalactivity patterns selectively associated with the emotional state,configured to subject a subject emotional arousal in control over theemotional state to a stimulus selected from the group consisting of oneor more of a sensory excitation, a peripheral excitation, a transcranialexcitation, and a deep brain stimulation, dependent on the processeddetermined neuronal activity pattern.

It is further an object to provide an apparatus for facilitating anemotional skill or emotional learning process, compromising: an input,configured to receive data representing a neuronal activity pattern of asubject while engaged in an emotional skill or emotional learningprocess; at least one automated processor, configured to process thedetermined neuronal activity pattern, to determine neuronal activitypatterns selectively associated with successful leaning of the emotionalskill or emotional learning process; and a stimulator, configured tosubject a subject emotional arousal in the respective emotional skill oremotional leaning process to a stimulus selected from the groupconsisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the processed determined neuronal activity pattern.

It is also an object to provide an apparatus for inducing of a desiredemotional state, compromising: an input, configured to receive datarepresenting a neuronal activity pattern of a skilled subject whileexperiencing the desired emotional state; at least one automatedprocessor, configured to process the determined neuronal activitypattern, to determine neuronal activity patterns selectively associatedwith the desired emotional state; and a stimulator, configured tosubject a recipient desiring to attain the same emotional state to astimulus selected from the group consisting of one or more of a sensoryexcitation, a peripheral excitation, a transcranial excitation, and adeep brain stimulation, dependent on the processed determined neuronalactivity pattern.

It is a further object to provide a system for influencing a brainelectrical activity pattern of a subject during emotional arousal,comprising: an input, configured to determine a target brain activitystate for the subject, dependent on the emotional state; at least oneprocessor, configured to generate a stimulation pattern profile adaptedto achieve the target brain activity state for the subject, dependent onthe emotional state; and a stimulator, configured to output at least onestimulus, proximate to the subject, dependent on the generatedstimulation pattern profile.

It is yet a further object to provide a system for influencing a brainelectrical activity pattern of a subject during experiencinginformation, comprising: an input, configured to determine a targetbrain activity state for the subject, dependent on the nature of therespective information; at least one processor, configured to generate astimulation pattern profile adapted to achieve the target brain activitystate for the subject, dependent on the emotion; and a stimulator,configured to output at least one stimulus, proximate to the subject,dependent on the generated stimulation pattern profile.

It is still a further object to provide a system for influencing a brainelectrical activity pattern of a subject during a state of emotionalarousal, comprising: an input, configured to determine a target brainemotional state for the subject, dependent on the desired emotionalstate; at least one processor, configured to generate a stimulationpattern profile adapted to achieve the target brain emotional state forthe subject, dependent on the emotional state; and a stimulator,configured to output at least one stimulus, proximate to the subject,dependent on the generated stimulation pattern profile.

It is a still further object to provide a system for determining atarget brain activity state for a subject, dependent on an emotionstate, comprising: a first monitor, configured to acquire a brainactivity of a first subject during the emotion state; at least one firstprocessor, configured to analyze a spatial brain activity state overtime of the first subject; and determine spatial brain activity statesof the first subject, which represent readiness for emotion state; asecond monitor, configured to acquire a brain activity of a secondsubject during performance of a variety of activities, under a varietyof stimuli; and at least one second processor, configured to: analyze aspatial brain activity state overtime of the second subject; andtranslate the determined spatial brain activity states of the firstsubject which represent readiness for the emotion state, into a stimuluspattern for the second subject to achieve a spatial brain activity statein the second subject corresponding to emotion state.

It is a still further object to provide a system for determining atarget brain activity state for a subject, dependent on an emotion ormood, comprising: a first monitor, configured to acquire a brainactivity of a first subject during experiencing the emotion or mood; atleast one first processor, configured to analyze a spatial brainactivity state over time of the first subject; and determine spatialbrain activity states of the first subject, which represent the emotionor mood; a second monitor, configured to acquire a brain activity of asecond subject during the emotion or mood, under a variety of stimuli;and at least one second processor, configured to: analyze a spatialbrain activity state overtime of the second subject; and translate thedetermined spatial brain activity states of the first subject whichrepresent the emotion or mood, into a stimulus pattern for the secondsubject to achieve a spatial brain activity state in the second subjectcorresponding to the emotion or mood.

It is a further object to provide a method of enhancing an emotionalstate of a first subject, the method comprising: recording a secondsubjects brainwaves EEG while at rest; having the second subjectexperience or enact an emotionally charged experience to induce anemotional state or mood; recording the second subjects brainwaves EEGwhile experiencing or enacting said emotionally charged experience;extracting a predominant temporal pattern associated with said emotionalstate from the recorded brainwaves by comparing them with the brainwavesat rest; encoding said temporal pattern as a digital code stored in atangible media; and using said digital code to modulate the temporalpattern on a signal perceptible to the first subject while said firstsubject is trying to attain the said emotional state, whereby saidperceptible signal stimulates in the second subject brainwaves havingsaid temporal pattern to induce the emotional state or mood.

It is still a further object to provide a method of enhancing anemotional state of a first person, the method comprising: recording asecond person's brainwaves or EEG while at rest or prior to achieving adesired emotional state; subjecting having the second person to theperformance; recording the second person's brainwaves or EEG whilesubject to the performance; extracting a predominant temporal patternassociated with said performance from the recorded brainwaves or EEG bycomparing them with the brainwaves or EEG at rest or prior to achievingthe desired emotional state; encoding said temporal pattern as a digitalcode stored in a tangible media; and using said digital code to modulatethe temporal pattern on a signal perceptible to the first person whilesaid first person is seeking to achieve said desired emotional state,whereby said light signal stimulates in the first subject brainwaves orEEG having said temporal pattern to enhance the achievement of thedesired emotional state.

A still further object provides a method of assisted appreciation of artby a first subject, the method comprising: recording a second subjectsbrainwaves EEG while at rest, wherein the second subject isknowledgeable in the art; having the second subject experience the art;recording the second subjects brainwaves (e.g., EEG, or MEG) whileexperiencing the art; extracting a predominant temporal patternassociated with appreciating the art from the recorded brainwaves bycomparing them with the brainwaves at rest; encoding said temporalpattern as a digital code stored in a tangible media; and using saiddigital code to modulate the temporal pattern on a signal perceptible tothe first subject while the first subject is seeking to appreciate theart, whereby said signal stimulates in the first subject brainwaveshaving said temporal pattern.

It is another object to provide a computer readable medium, storingtherein non-transitory instructions for a programmable processor toperform a process, comprising the computer-implemented steps:synchronizing brain activity data of a subject with at least one eventinvolving the subject; analyzing the brain activity data to determine aselective change in the brain activity data corresponding to anemotional correlate of the event and determine a stimulation patternadapted to induce a brain activity having a correspondence to the brainactivity data associated with the emotion, based on at least a brainactivity model.

The at least one of a sensory excitation, peripheral excitation, andtranscranial excitation may be generated based on a digital code. Thesubjecting of the subject having the emotion or mood to the sensoryexcitation increases a rate of achieving the emotion in the targetsubject. Similarly, the subjecting of the subject seeking to achieve theemotion or mood to the sensory excitation increases a rate of achievingthe emotion or mood in the target. Likewise, the subjecting of thesubject seeking to achieve the respective emotional state to the sensoryexcitation improves the quality or intensity of the emotional state inthe subject.

The method may further comprise determining a neuronal baseline activityof the skilled subject while not engaged in the emotion, a neuronalbaseline activity of the subject, a neuronal activity of the skilledsubject while engaged in the emotion, and/or a neuronal activity of thesubject while engaged in the emotion.

The representation of the processed the determined neuronal activitypattern may be stored in memory. The storage could be on a tangiblemedium as an analog or digital representation. It is possible to storethe representation in a data storage and access system either for apermanent backup or further processing the respective representation.The storage can also be in a cloud storage and/or processing system.

The neuronal activity pattern may be obtained by electroencephalography,magnetoencephalography, MRI, fMRI, PET, low-resolution brainelectromagnetic tomography, or other electrical or non-electrical means.

The neuronal activity pattern may be obtained by at least one implantedcentral nervous system (cerebral, spinal) or peripheral nervous systemelectrode. An implanted neuronal electrode can be either within theperipheral nervous system or the central nervous system. The recordingdevice could be portable or stationary. Either with or without onboardelectronics such as signal transmitters and/or amplifiers, etc. The atleast one implanted electrode can consist of a microelectrode arrayfeaturing more than one recording site. Its main purpose can be forstimulation and/or recoding.

The neuronal activity pattern may be obtained by at least a galvanicskin response. Galvanic skin response or resistance is often alsoreferred as electrodermal activity (EDA), psychogalvanic reflex (PGR),skin conductance response (SCR), sympathetic skin response (SSR) andskin conductance level (SCL) and is the property of the human body thatcauses continuous variation in the electrical characteristics of theskin.

The stimulus may comprise a sensory excitation. The sensory excitationmay by either sensible or insensible. It may be either peripheral ortranscranial. It may consist of at least one of a visual, an auditory, atactile, a proprioceptive, a somatosensory, a cranial nerve, agustatory, an olfactory, a pain, a compression and a thermal stimulus ora combination of the aforesaid. It can, for example, consist of lightflashes either within ambient light or aimed at the subjects eyes, 2D or3D picture noise, modulation of intensity, within the focus of thesubjects eye the visual field or within peripheral sight Within a videopresentation, intensity variations may be provided around a periphery ofthe presentation, globally throughout a presentation (i.e., modulating abacklight or display intensity), or programmed to modulate a brightnessof individual objects.

The stimulus may comprise a peripheral excitation, a transcranialexcitation, a sensible stimulation of a sensory input, an insensiblestimulation of a sensory input, a visual stimulus, an auditory stimulus,a tactile stimulus, a proprioceptive stimulus, a somatosensory stimulus,a cranial nerve stimulus, a gustatory stimulus, an olfactory stimulus, apain stimulus, an electric stimulus, a magnetic stimulus, or a thermalstimulus.

The stimulus may comprise transcranial magnetic stimulation (TMS),cranial electrotherapy stimulation (CES), transcranial direct currentstimulation (tDCS), comprise transcranial alternating currentstimulation (tACS), transcranial random noise stimulation (tRNS),comprise transcranial pulsed current stimulation (tPCS), pulsedelectromagnetic field, or noninvasive or invasive deep brain stimulation(DBS), for example. The stimulus may comprise transcranial pulsedultrasound (TPU). The stimulus may comprise a cochlear implant stimulus,spinal cord stimulation (SCS) or a vagus nerve stimulation (VNS), orother direct or indirect cranial or peripheral nerve stimulus. Thestimulus may comprise or achieve brainwave entrainment. The stimulus maycomprise electrical stimulation of the retina, a pacemaker, astimulation microelectrode array, electrical brain stimulation (EBS),focal brain stimulation (FBS), light, sound, vibrations, anelectromagnetic wave. The light stimulus may be emitted by at least oneof a light bulb, a light emitting diode (LED), and a laser. The signalmay be one of a ray of light, a sound wave, and an electromagnetic wave.The signal may be a light signal projected onto the first subject by oneof a smart bulb generating ambient light, at least one LED position nearthe eyes of the first subject and laser generating low-intensity pulses.

The mental state may be associated with learning or performing a skill.The skill may comprise a mental skill, e.g., cognitive, alertness,concentration, attention, focusing, memorization, visualization,relaxation, meditation, speedreading, creative skill,“Whole-brain-thinking”, analytical, reasoning, problem-solving, criticalthinking, intuitive, leadership, learning, speedreading, patience,balancing, perception, linguistic or language, language comprehension,quantitative, “fluid intelligence”, pain management, skill ofmaintaining positive attitude, a foreign language, musical, musicalcomposition, writing, poetry composition, mathematical, science, art,visual art, rhetorical, emotional control, empathy, compassion,motivational skill, people, computational, science skill, or aninventorship skill. See, U.S. Patent and Pub. App. Nos. U.S. Pat. Nos.6,435,878, 5,911,581, and 20090069707. The skill may comprise a motorskill, e.g., fine motor, muscular coordination, walking, running,jumping, swimming, dancing, gymnastics, yoga; an athletic or sports,massage skill, martial arts or fighting, shooting, self-defense; speech,singing, playing a musical instrument, penmanship, calligraphy, drawing,painting, visual, auditory, olfactory, game-playing, gambling,sculptor's, craftsman, massage, or assembly skill. Where a skill is tobe enhanced, and an emotion to be achieved (or suppressed),concurrently, the stimulus to the recipient may be combined in such away as to achieve the result In some cases, the component is universal,while in others, it is subjective. Therefore, the combination mayrequire adaptation based on the recipient characteristics.

The technology may be embodied in apparatuses for acquiring the brainactivity information from the source, processing the brain activityinformation to reveal a target brain activity state and a set ofstimuli, which seek to achieve that state in a recipient, and generatingstimuli for the recipient to achieve and maintain the target brainactivity state over a period of time and potential state transitions.The generated stimuli may be feedback controlled. A general-purposecomputer may be used for the processing of the information, amicroprocessor, a FPGA, an ASIC, a system-on-a-chip, or a specializedsystem, which employs a customized configuration to efficiently achievethe information transformations required. Typically, the source andrecipient act asynchronously, with the brain activity of the sourcerecorded and later processed. However, real-time processing and brainactivity transfer are also possible. In the case of a general purposeprogrammable processor implementation or portions of the technology,computer instructions may be stored on a nontransient computer readablemedium. Typically, the system will have special-purpose components, suchas a transcranial stimulator, or a modified audio and/or display system,and therefore the system will not be a general purpose system. Further,even in a general purpose system the operation per se is enhancedaccording to the present technology.

It is another object to provide a method of teaching one of anemotion-dependent mental skill and a motor skill to a first subject, themethod comprising: recording a second subjects brainwaves EEG while atrest; having the second subject perform said one of a mental skill and amotor skill; recording the second subjects brainwaves while performingsaid one of a mental skill and a motor skill; extracting a predominanttemporal pattern associated with said one of a mental skill and a motorskill from the recorded brainwaves by comparing them with the brainwavesat rest; encoding said temporal pattern together with an emotional statetargeting stimulus pattern, as a digital code stored in a tangiblemedia; and using said digital code to modulate the temporal pattern on asignal perceptible to the first subject while the first subject islearning said one of a mental and a motor skill, whereby said lightsignal stimulates in the first subject brainwaves having said temporalpattern to accelerate learning of said one if a mental skill and a motorskill. The emotional state targeting stimulus pattern may be derivedfrom the first subject, the second subject, or a one or more differentsubjects. The stimulation pattern may thus be modified from the secondsubject pattern to bias the first subject toward a desired emotionalstate.

It is a further object to provide a high-definition transcranialalternating current stimulation (HD-tACS) stimulation of a target,having a stimulation frequency, amplitude pattern, spatial pattern,dependent on an existing set of states in the target and a set ofbrainwave patterns from a target engaged in a mood, adapted to improvean emotional state or mood of the recipient.

It is yet another object to provide a system and method for facilitatinga mental process, compromising: determining a neuronal activity pattern,of a subject while engaged in an emotional process; processing thedetermined neuronal activity pattern with at least one automatedprocessor and subjecting a subject targeting the emotional process to astimulus selected from the group consisting of one or more of a sensoryexcitation, a peripheral excitation, a transcranial excitation, and adeep brain stimulation, dependent on the processed electromagneticdetermined neuronal activity pattern while the subject is subjected totES, a psychedelic and/or other pharmaceutical agents.

It is a still further object to provide a method of facilitating a skilllearning process, comprising: determining a neuronal activity pattern ofa skilled subject while engaged in a respective skill; processing thedetermined neuronal activity pattern with at least one automatedprocessor modifying the determined neuronal activity pattern accordingto an emotional state neuronal activity pattern; and subjecting asubject training in the respective skill to a stimulus selected from thegroup consisting of one or more of a sensory excitation, a peripheralexcitation, a transcranial excitation, and a deep brain stimulation,dependent on the modified processed determined neuronal activitypattern. The transcranial electric stimulation (tES) may be one oftranscranial direct current stimulation (tDCS), transcranial alternativecurrent stimulation (tACS), and high-definition transcranial alternativecurrent stimulation (tES). The emotional state neuronal activity patternmay be a pattern that increases alertness and focus, for example.

Another object provides a method of facilitating a skill learningprocess, compromising: determining a respective neuronal activitypattern of a skilled subject while engaged in a respective skill andhaving an emotional state appropriate for learning the skill and whileengaged in the respective skill and not having the emotional stateappropriate for learning the skill; processing the determined neuronalactivity pattern with at least one automated processor subjecting asubject training in the respective skill to one of a pharmaceuticalagent and a psychedelic agent and subjecting a subject training in therespective skill to a stimulus selected from the group consisting of oneor more of a sensory excitation, a peripheral excitation, a transcranialexcitation, and a deep brain stimulation, dependent on the processeddetermined neuronal activity pattern while engaged in a respective skilland having an emotional state appropriate for learning the skill, andadapting the stimulus based on feedback based on a measurement of aneuronal activity pattern of the subject training in the respectiveskill to determine an emotional state of the subject training in therespective skill.

It is another object to provide a method of inducing an emotional statein a target subject, comprising determining a desired emotional state;selecting a profile from a plurality of profiles stored in a memory, theplurality of profiles each corresponding to a brain activity pattern ofa donor subject having a respective emotional state; and exposing thetarget subject to at least one stimulus modulated according to theselected profile representing and being adapted to induce, in the targetsubject, the desired emotional state. The brain activity pattern may beat least one of an electroencephalographic brainwave pattern and amagnetoencephalographic brainwave pattern. The at least one stimulus maystimulate a cranial nerve of the target subject. The at least onestimulus may comprise at least one of a visual stimulus, and an auditorystimulus, a two-channel auditory stimulus adapted to induce binauralbeats, at least one of a tactile stimulus and a proprioceptive stimulus,an at least one of a direct electrical current and an alternatingelectrical current, and/or a magnetic field. The stimulus may compriseat least one of an auditory stimulus and a visual stimulus with afrequency corresponding to at least a frequency pattern in a brainwavepattern of the donor subject.

The desired emotional state may be one of happiness, joy, gladness,cheerfulness, bliss, delight, ecstasy, optimism, exuberance, merriment,joviality; vivaciousness, pleasure, excitement, sexual arousal,relaxation, harmony, and peace.

The target subject may be the same as or different from the donorsubject. The target subject may be identical with the donor subject,wherein the brain activity pattern of the donor subject was recordedprior to the exposing the target subject to at least one stimulus.

The at least one stimulus may comprise a dynamically changingelectromagnetic field adapted to synchronize the target subjectsbrainwave pattern with a brainwave pattern of the donor subject havingthe desired emotional state.

The selected profile may be derived from recording of brainwave patternsof the donor subject selectively acquired during the desired emotionalstate. The selected profile may comprise a model derived from at leastone of a spatial, a frequency and a phase analysis of the recordedbrainwave patterns.

The method may further comprise recording EEG signals of the donorsubject in the desired emotional state; decoding at least one of atemporal and a spatial pattern from the recorded EEG signals; andstoring the decoded at least one of temporal and spatial pattern in anon-volatile memory as at least one profile.

The method may comprise selectively modifying stimulus based ondifferences between the donor subject, from which the profile may bederived, and the target subject.

The stimulus may comprise applying at least one of a temporal and aspatial electrical stimulation pattern to the target subject viatranscranial electrical stimulation (TES) to induce the desiredemotional state. The transcranial electrical stimulation (TES) may be atleast one of a transcranial direct current stimulation (tDCS), anoscillating transcranial direct current stimulation (osc-tDCS), atranscranial alternating current stimulation (tACS), a transcranialpulsed current stimulation (tPCS), and a transcranial random noisestimulation (tRNS).

The profile may be derived from brain activity pattern of the donorsubject comprising a magnetoencephalogram (MEG), and the stimulus maycomprise applying a spatial magnetic stimulation pattern to the targetsubject via transcranial magnetic stimulation (TMS) to induce thedesired emotional state.

The stimulus may achieve brain entrainment in the target subject.

The method may further comprise determining a second desired emotionalstate; selecting a second profile from the plurality of profiles storedin a memory; and exposing the target subject to a stimulus modulatedaccording to the selected second profile, representing and being adaptedto induce, in the target subject, the second desired emotional state,the second emotional state being different from the emotional state andbeing induced in succession after the emotional state.

At least one profile may correspond to consensus brain activity patternof a plurality of donor subjects, each of the plurality of donorsubjects having the respective emotional state.

It is a further object to provide a method of brainwave entrainmentcomprising: recording brainwaves of a first subject in a desiredemotional state; decoding at least one of a temporal and a spatialpattern from the brainwaves; storing a representation of the pattern ina memory; retrieving said pattern from the memory; modulating thedecoded at least one of the temporal and the spatial pattern on at leastone stimulus signal; and applying said at least one stimulus signal to asecond subject, to induce the second subject to assume the emotionalstate. The step of recording brainwaves comprise recording of at leastone of electroencephalogram and magnetoencephalogram of the brainwaves.The stimulus signal may be at least one of a direct current and analternating current, and said applying may comprise applying said atleast one of a direct current and an alternating current to the secondsubject via respectively a transcranial direct current stimulation(tDCS) or transcranial alternating current stimulation (tACS) to inducethe desired emotional state.

It is a still further object to provide a method of brainwaveentrainment comprising: recording the brainwaves of a first subject in adesired emotional state; decoding at least one of temporal and spatialpattern from the recorded brainwaves; storing said at least one of thetemporal and spatial pattern in a memory; retrieving said at least oneof the temporal and spatial pattern from the memory; modulating the atleast one of the temporal and spatial pattern on at least one of acurrent, a magnetic field, a light signal, and an acoustic signal; andexposing the second subject to the at least one of the current, themagnetic field, the light signal, and the acoustic signal, to induce thedesired emotional state.

The step of recording the brainwaves may comprise recording of at leastone of an electroencephalogram and a magnetoencephalogram of thebrainwaves.

Another object provides a method of recording a desired emotional statefrom a donor, comprising: determining an emotional state of the donor ifthe donor may be in the desired emotional state, recording neuralcorrelates of the emotional state of the donor analyzing neuralcorrelates of the desired emotional state of the donor to decode atleast one of a temporal and a spatial pattern corresponding to thedesired emotional state; converting said at least one of a temporal anda spatial pattern corresponding to the desired emotional state into aneurostimulation pattern; and storing the neurostimulation pattern inthe nonvolatile memory. The neural correlates may be brainwaves of thedonor.

The step of analyzing neural correlates may comprise identifyingprincipal components of the brainwaves. The identifying of principalcomponents may comprise performing one of a principal component analysis(PCA), a curvilinear principal component analysis, an independentcomponent analysis (ICA), a Karhunen-Loeve transform (KLT), a singularvalue decomposition (SVD), and a Factor analysis. The step of analyzingneural correlates may comprise performing a frequency domain analysis.The step of performing the frequency analysis may comprise performingone of a Fourier Transform, a Laplace Transform, a Fourier-Stieljestransform, a Gelfand transform, time-frequency analysis, a short-timeFourier transform, and a fractional Fourier transform.

The desired emotional state may be one of of happiness, joy, gladness,cheerfulness, bliss, delight, ecstasy, optimism, exuberance, merrimentjoviality; vivaciousness, pleasure, excitement, sexual arousal,relaxation, harmony, and peace.

The method may further comprise retrieving the neurostimulation patternfrom the nonvolatile memory; and stimulating the recipients brain withat least one stimulus modulated with the neurostimulation pattern toinduce the desired emotional state in the recipient.

The at least one stimulus may be one of a direct current, an alternatingcurrent, a magnetic field, a light, a sound, a tactile signal and anolfactory signal.

The recipient may be the donor at a point in time subsequent to the timeof recording the neural correlates of the emotional state of the donor.

The method may further comprise determining an emotional state of therecipient to confirm that the recipient may be in the desired emotionalstate. The method may further comprise developing a brain model of therecipient and adjusting said at least one stimulus in accordance withthe model to adjust for the differences between the recipients brain andthe donor's brain. The method may further comprise the step ofadministering a pharmacological agent to the recipient to facilitateresponse of the recipient to the at least one stimulus to induce thedesired emotional state. The method may further comprise performing, bythe recipient, a physical exercise in conjunction with the at least onestimulus.

It is another object to provide a relational database of neuralcorrelates of emotional states, comprising a first table storing aplurality of respective emotional states, linked with a second tablestoring information associated with respective emotional states obtainedby: recording neural correlates of the respective emotional state ofeach of a plurality of donors while in the respective emotional state;decoding from the recorded neural correlates at least one of a temporaland a spatial pattern corresponding to the plurality of respectiveemotional states; and storing information selectively derived from theat least one of the temporal and the spatial pattern corresponding tothe plurality of respective emotional states in the second table. Theneural correlates of each respective emotional state may be brainwaves.The recording of neural correlates may be done by using one of anelectroencephalogram and a magnetoencephalogram. The relational databasemay be accessible by receipt of a respective emotional state andresponsive by providing information linked to the respective emotionalstate.

Another object provides a method of increasing emotional emersion in apresentation, comprising: defining a target emotional state associatedwith at least a portion of the presentation; retrieving a record from adatabase associated with the target emotional state, derived fromrecorded neural correlates of donors engaged in the target emotionalstate; defining a neurostimulation pattern based on the record retrievedfrom the database; and subjecting a recipient to the definedneurostimulation pattern concurrent with being presented with the atleast a portion of the presentation.

The defining a target emotional state associated with at least a portionof the presentation may comprise defining a series of emotional statessynchronized with activity or objects depicted in the presentation. Theretrieving of the record from the database associated with the targetemotional state may comprise retrieving a plurality of recordscorresponding to the series of emotional states. The defining of theneurostimulation pattern may comprise defining a series ofneurostimilation patterns based on the retrieved plurality of records.The subjecting the recipient to the defined neurostimulation patternconcurrent with being presented with the at least a portion of thepresentation may comprise subjecting the recipient to the defined seriesof neurostimulation patterns, temporally synchronized with the portionsof presentation, in an order defined by the presentation.

The target emotional state may be defined by an author of thepresentation, or automatically derived from the presentation.

The database may be a relational database, having a first table ofrespective emotional states, and a second table of information relatingto neural correlates of the respective emotional states, the first tableand the second table being linked together and searchable based onrespective emotional state.

At least one record of the database may be derived from recorded neuralcorrelates of a plurality of different donors engaged in a commonrespective target emotional state. The at least one record may comprisea consensus of the plurality of different donors. The at least onerecord may comprise a plurality of sub-records, each sub-record beingderived from a distinct subpopulation of the plurality of differentdonors, further comprising determining a characteristic of therecipient, and selecting a respective sub-record from the record basedon the determined characteristic.

The neurostimulation pattern may be at least one of an electricalcurrent, a magnetic field, a light signal, and an acoustic signal. Theneurostimulation pattern may be encoded in the record and/or may bedefined by at least one automated processor after retrieving the record,and in selective dependence on at least one characteristic of therecipient. The presentation may comprise an audiovisual presentation,e.g., a virtual reality presentation. The defined neurostimulationpattern may be encoded as at least one of an audio and a visual stimuluswithin the audiovisual presentation. The defined neurostimulationpattern may be encoded as the at least one of the audio and the visualstimulus within the audiovisual presentation dependent on at least onecharacteristic of the recipient. The defined neurostimulation patternmay be dependent on automatically generated or manual feedback from therecipient.

Another object provides a system for increasing emotional response to apresentation, comprising: a database comprising a record associated witha target emotional state, the record being derived from recorded neuralcorrelates of at least one donor engaged in the respective targetemotional state; at least one input configured to receive an associationof the target emotional state with a portion of a presentation; at leastone automated processor configured to define a neurostimulation patternbased on the record retrieved from the database; and a neurostimulator,configured to emit the defined neurostimulation pattern concurrent withpresentation of the portion of the presentation.

The input may be configured to receive a series of associations ofrespective target emotional states with respective portions of thepresentation, and the neurostimulator may be configured to emit a seriesof the defined neurostimulation patterns synchronized with the receivedseries of associations of the respective target emotional states withthe respective portions of the presentation. The database may be arelational database, having a first table of respective emotionalstates, and a second table of information relating to neural correlatesof the respective emotional states, the first table and the second tablebeing linked together and searchable based on respective emotionalstate. At least one record may be derived from recorded neuralcorrelates of a plurality of different donors engaged in a commonrespective target emotional state. The at least one record may comprisea consensus of the plurality of different donors. The at least onerecord may comprise a plurality of sub-records, each sub-record beingderived from a distinct subpopulation of the plurality of differentdonors, a respective sub-record being selectable from the record basedon the determined characteristic. The neurostimulator may be at leastone of an electrical current stimulator, a magnetic field stimulator, alight signal stimulator, and an acoustic signal stimulator. Theneurostimulation pattern may be encoded in the record, and/or may bedefined by the at least one automated processor dependent on the record,and in selective dependence on at least one characteristic of therecipient. The presentation may comprise an audiovisual presentation,e.g., a virtual reality presentation, and optionally the definedneurostimulation pattern may be encoded as at least one of an audio anda visual stimulus within the audiovisual presentation. The definedneurostimulation pattern may be encoded as the at least one of the audioand the visual stimulus within the audiovisual presentation dependent onat least one characteristic of the recipient. The definedneurostimulation pattern may be dependent on automatically or manuallygenerated feedback from the recipient.

Other objects will become apparent from a review of disclosure hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference number in different figures indicates similaror identical items.

FIG. 1 shows the electric activity of a neuron contributing to abrainwave.

FIG. 2 shows transmission of an electrical signal generated by a neuronthrough the skull, skin and other tissue to be detectable by anelectrode transmitting this signal to EEG amplifier.

FIG. 3 shows an illustration of a typical EEG setup with a subjectwearing a cup with electrodes connected to the EEG machine, which is, inturn, connected to a computer screen displaying the EEG.

FIG. 4 shows a typical EEG reading.

FIG. 5 shows one second of a typical EEG signal.

FIG. 6 shows main brainwave patterns.

FIGS. 7-13 shows a flowchart according to embodiments of the invention.

FIG. 14 shows a schematic representation of an apparatus according toone embodiment of the invention.

FIG. 15 shows brainwave real-time BOLD (Blood Oxygen Level Dependent)fMRI studies acquired with synchronized stimuli.

FIG. 16 shows Brain Entrainment Frequency Following Response (or FFR).

FIG. 17 shows brainwave entrainment before and after synchronization.

FIG. 18 shows brainwaves during inefficient problem solving and stress.

FIGS. 19 and 20 show how binaural beats work.

FIG. 21 shows Functional Magnetic Resonance Imaging (Mental states maybe induced in a subject)

FIG. 22 shows a photo of a brain forming a new idea.

FIG. 23 shows 3D T2 CUBE (SPACE/VISTA) FLAIR & DSI tractography

FIG. 24 shows an EEG tracing.

FIGS. 25-29 show flowcharts according to embodiments of the invention.

FIG. 30 shows human brain anatomy.

FIG. 31 shows a brain map.

FIG. 32 shows an image depicting neuron anatomy.

FIG. 33 shows graphs representing a dimensional view of emotions.

FIG. 34 shows a representation of neural activity with respect toemotional state.

FIGS. 35-41 show flowcharts according to embodiments of the invention.

FIG. 42 shows graphs of tDCS. tRNS, and tACS stimulation patterns.

FIGS. 43 and 44 show representations of tDCS neural stimulation.

FIG. 45 shows a representation of tACS or tRNS neural stimulation.

FIG. 46 shows a representation of intracranial electrode implantation.

FIG. 47 shows a representation of tDCS electrode location.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings so that the presentdisclosure may be readily implemented by those skilled in the art.However, it is to be noted that the present disclosure is not limited tothe embodiments but can be embodied in various other ways. In drawings,parts irrelevant to the description are omitted for the simplicity ofexplanation, and like reference numerals denote like parts through thewhole document.

Through the whole document, the term “connected to” or “coupled to” thatis used to designate a connection or coupling of one element to anotherelement includes both a case that an element is “directly connected orcoupled to” another element and a case that an element is“electronically connected or coupled to” another element via stillanother element. Further, it is to be understood that the term“comprises or includes” and/or “comprising or including” used in thedocument means that one or more other components, steps, operationand/or existence or addition of elements are not excluded in addition tothe described components, steps, operation and/or elements unlesscontext dictates otherwise.

Through the whole document, the term “unit” or “module” includes a unitimplemented by hardware or software and a unit implemented by both ofthem. One unit may be implemented by two or more pieces of hardware, andtwo or more units may be implemented by one piece of hardware.

Other devices, apparatus, systems, methods, features and advantages ofthe invention will be or will become apparent to one with skill in theart upon examination of the following figures and detailed description.It is intended that all such additional systems, methods, features andadvantages be included within this description, be within the scope ofthe invention, and be protected by the accompanying claims.

The present invention generally relates to enhancing emotional responseby a subject in connection with the received information by conveying tothe brain of the subject temporal patterns of brainwaves of a secondsubject who had experienced such emotional response, said temporalpattern being provided non-invasively via light, sound, transcranialdirect current stimulation (tDCS), transcranial alternating currentstimulation (tDAS) or HD-tACS, transcranial magnetic stimulation (TMS)or other means capable of conveying frequency patterns.

The transmission of the brainwaves can be accomplished through directelectrical contact with the electrodes implanted in the brain orremotely employing light, sound, electromagnetic waves and othernon-invasive techniques. Light, sound, or electromagnetic fields may beused to remotely convey the temporal pattern of prerecorded brainwavesto a subject by modulating the encoded temporal frequency on the light,sound or electromagnetic filed signal to which the subject is exposed.

Every activity, mental or motor, and emotion is associated with uniquebrainwaves having specific spatial and temporal patterns, i.e., acharacteristic frequency or a characteristic distribution of frequenciesover time and space. Such waves can be read and recorded by severalknown techniques, including electroencephalography (EEG),magnetoencephalography (MEG), exact low-resolution brain electromagnetictomography (eLORETA), sensory evoked potentials (SEP), fMRI, functionalnear-infrared spectroscopy (fNIRS), etc. The cerebral cortex is composedof neurons that are interconnected in networks. Cortical neuronsconstantly send and receive nerve impulses-electrical activity-evenduring sleep. The electrical or magnetic activity measured by an EEG orMEG (or another device) device reflects the intrinsic activity ofneurons in the cerebral cortex and the information sent to it bysubcortical structures and the sense receptors.

An EEG electrode mainly detects the neuronal activity in the brainregion just beneath it. However, the electrodes receive the activityfrom thousands of neurons. One square millimeter of cortex surface, forexample, has more than 100,000 neurons. It is only when the input to aregion is synchronized with electrical activity occurring at the sametime that simple periodic waveforms in the EEG become distinguishable.

The spatial and temporal pattern associated with specific brainwaves canbe digitized and encoded in software code. It has been observed that“playing back the brainwaves” to another animal or person by providingdecoded temporal pattern through transcranial direct current stimulation(tDCS), transcranial alternating current stimulation (tACS), highdefinition transcranial alternating current stimulation (HD-tDCS),transcranial magnetic stimulation (TMS), or through electrodes implantedin the brain allows the recipient to achieve the emotional or mentalstate at hand or to increase a speed of achievement. For example, if thebrainwaves of a mouse navigated a familiar maze are decoded (by EEG orvia implanted electrodes), playing this temporal pattern to anothermouse unfamiliar with this maze will allow it to learn to navigate thismaze faster.

Similarly, recording brainwaves associated with a specific emotional ormental response of one subject and later “playing back” this response toanother subject will induce a similar emotional or mental response inthe second subject More generally, when one animal assumes an emotionalor mental state, parts of the brain will have characteristic activitypatterns. Further, by “artificially” inducing the same pattern inanother animal, the other animal will have the same emotional or mentalstate, or more easily be induced into that state. The pattern ofinterest may reside deep in the brain, and thus be overwhelmed in an EEGsignal by cortical potentials and patterns. However, techniques otherthan surface electrode EEG may be used to determine and spatiallydiscriminate deep brain activity, e.g., from the limbic system. Forexample, various types of magnetic sensors may sense deep brainactivity. See, e.g., U.S. Pat. Nos. 9,618,591; 9,261,573; 8,618,799; and8,593,141.

In some cases, EEGs dominated by cortical excitation patterns may beemployed to sense the emotional or mental state, since the corticalpatterns may correlate with lower-level brain activity. Note that thedetermination of a state representation of an emotional or mental neednot be performed each time the system is used; rather, once the brainspatial and temporal activity patterns and synchronization statesassociated with a particular emotional or mental states are determined,those patterns may be used for multiple targets and over time.

Similarly, while the goal is, for example, to trigger the target toassume the same brain activity patterns are the exemplar, this can beachieved in various ways, and these methods of inducing the desiredpatterns need not be invasive. Further, user feedback, especially in thecase of a human emotional or mental state transferee, may be used totune the process. Finally, using the various senses, especially sight,sound, vestibular, touch, proprioception, taste, smell, vagus afferent,other cranial nerve afferent, etc. can be used to trigger high levelmental activity, that in a particular subject achieves the desired metalstate, emotion or mood.

Thus, in an experimental subject, which may include laboratory scaleand/or invasive monitoring, a set of brain electrical activity patternsthat correspond to particular emotions or emotional or mental states isdetermined. Preferably, these are also correlated with surface EEGfindings. For the transferee, a stimulation system is provided that isnon-hazardous and non-invasive. For example, audiovisual stimulation maybe exclusively used. A set of EEG electrodes is provided to measurebrain activity, and an adaptive or genetic algorithm scheme is providedto optimize the audiovisual presentation, seeking to induce in thetransferee the target pattern found in the experimental subject Afterthe stimulation patterns, which may be path dependent, are determined, iis likely that these patterns will be persistent, though over longertime periods, there may be some desensitization to the stimulationpattern(s). In some cases, audiovisual stimulation is insufficient, andTMS or other electromagnetic stimulation (superthreshold, or preferablysubthreshold) is employed to assist in achieving the desired state andmaintaining it for the desired period.

Such technology can be used to significantly enhance the emotionalresponse to viewing photos, reproduction of art, virtual reality, TV,listening to music, reading a book, etc. The user's emotional state maybe primed for the secondary stimulation, to enhance the results.

For example, when a movie is filmed, actors get into their roles andexperience real emotions. If we record these emotions by recording theirbrainwaves during acting and later playing them back to viewers orotherwise induce in the viewers the same emotional states, while theyare watching the film, this would significantly enhance the experience.As discussed above, the emotional state of an actor may be determinedbased on a script, facial recognition, explicit statement of the actor,etc., and need not be deciphered from the EEG.

Similarly, while producing virtual reality, we can couple digital filescontaining video with files of brainwaves of people present during therecording, who see the nature in real time and experience emotions firsthand, which would dramatically enhance VR experience.

In another example, a book or an eBook can be coupled with a file ofrecorded brainwaves of the writer or an experienced actor who is trainedto evoke an emotional response while reading a script may provide thestimulus.

One of the challenges of adapting robotic technology and artificialintelligence (AI) is a typical lack of an emotional response by a humansubject to a robot or an AI software agent Using brainwaves can helpevoke a positive emotional response in humans while interacting withrobots and/or AI agents.

One purpose of this invention is to enhance an emotional response by asubject while engaged in mood. Yet another purpose of this invention isto enhance an emotional response by a subject while engaged inentertainment Still another purpose of this invention is to enhance anemotional response by a subject while engaged with a robot or anartificial intelligence, another purpose of this invention is to assista person with recalling a past experience, still another purpose of thisinvention is to assist a person suffering from a form of dementia torecognize the person's family members and friends.

It may be difficult for many to experience the emotional response to arepresentation of an experience as to the genuine experience. Looking ata photograph of a Grand Canyon does not elicit the same emotionalresponse as seeing the Grand Canyon itself. Looking at a reproduction ofMona Lisa does not elicit the same emotional response as seeing theoriginal painting in Louvre. An immersive experience achieved throughvirtual reality (VR) applications goes a long way in simulating thereality, but still falls short of eliciting the emotional responsecomparable with the one associated with real experience.

Elderly people suffering from Alzheimer's disease or other forms ofdementia have difficult recalling their past experiences and recognizedfamily members and friends. While in the early stages of the diseasethey may have difficulty recalling the person's name or identity, butthey still recognize a family member as a loved one responding to seeinga family member with a positive emotion. In later stages, however, thepatients no longer feel the emotional response upon seeing a familymember and are frightened as if seeing a total stranger.

Recording brainwaves while a person is experiencing a strong emotionalresponse to a genuine experience and later transmitting these recordedbrainwaves to another or same individual may help experience strongeremotional response. For example, recording brainwaves of a person seeingfor the first time the Grand Canyon and transmitting these brainwaves toanother (or the same) person who is viewing a photograph of the GrandCanyon or viewing it through VR glasses would enhance the emotionalresponse of that person and help create more genuine immersiveexperience. Similarly, recording brainwaves of a person seeing for thefirst time the original painting of Mona Lisa in the Louvre andtransmitting these brainwaves to another (or the same) person who isviewing a reproduction of this painting or on a virtual museum tour ofthe Louvre viewing it through VR glasses would enhance the emotionalresponse of that person and help create more genuine immersiveexperience.

In another example, recording brainwaves of a musician playing the musicin a concert and transmitting these brainwaves to another person who islistening to a recording of this music would enhance the emotionalresponse of that person and help create more genuine immersiveexperience.

In a further example, recording brainwaves of actors while acting inmovie and transmitting these brainwaves to viewers who are watching themovie in a theater, on a television, on a computer, or through VRglasses would enhance the emotional response of that person and helpcreate more genuine immersive experience.

A further example provides that brainwaves associated with specificemotions may be recorded from actors asked to experience these emotions.A library of brainwaves corresponding to specific emotions can beassembled and used to enhance emotional response, for example, of agamer playing a computer game, with sequences of emotions triggered inthe gamer according to the context or paradigm of the game. There aremany applications where such library of brainwaves can be use. Examplesinclude use by law enforcement in helping deescalate a conflict ordiffuse a situation by calming down people involved in the conflict orsituation. It can be used by health care providers in the hospitals tohelp patients maintain positive attitude so important to their recovery.It can be used by personnel in psychiatric wards in calming downpsychiatric patient without the use of psychotropic medications. It canbe used in spas and meditation retreats or by individuals wishing toachieve the relaxation response to induce feeling of peace and calm or,perhaps, even the altered state of consciousness. It can be used byathletes, creative people, scientists and other wishing to get into the“zone” to achieve pick performance or creative inspiration.

In another example, recording brainwaves of a passionate teacherenthusiastically explaining a difficult subject and transmitting thesebrainwaves to a student who is studying the same subject would enhancethe emotional response of that person and help maintain focus,concentration, interest and may even help understand the subject ofstudy.

In a further example, recording brainwaves associated with the emotionalresponse of a person to his family members or friends while in theinitial stages of the Alzheimer's disease or another form of dementiaand later transmitting these brainwaves to the same person while in alater stages of the disease may help the patient recognize the familiarfaces or, at least, create a positive emotional response upon seeingfamily members reducing the fear and anxiety associate with inability torecognize familiar faces typical for the later stages of Alzheimer'sdisease and dementia.

The transmission of the brainwaves can be accomplished through directelectrical contact with the electrodes implanted in the brain orremotely employing light, sound, electromagnetic waves and othernon-invasive techniques.

Light, sound or invisible electromagnetic fields may be used to remotelyconvey the temporal pattern of prerecorded brainwaves to a subject, bymodulating the encoded temporal frequency on the light, sound orelectromagnetic filed signal to which the subject is exposed.

Another embodiment is combining a text with the code encoding thetemporal pattern of brainwaves of a person reading the text who hasnormal or accentuated affect. Say a user is reading a lengthy text (alegal brief or an eBook) on a computer screen. While displaying the textcomputer monitor (or another light source) generates light frequencycorresponding to the temporal pattern of brainwaves of another personreading the same text, prerecorded and embedded with the text. Theresult is speed reading and improved comprehension and retention of theinformation while achieving the same emotional states as the otherperson. This may have use in persons with abnormal psyche, who fail toachieve normal emotional response to media.

Employing light, sound or electromagnetic field to remotely convey thetemporal pattern of brainwaves (which may be prerecorded) to a subjectby modulating the encoded temporal frequency on the light, sound orelectromagnetic filed signal to which the subject is exposed.

When a group of neurons fires simultaneously, the activity appears as abrainwave. Different brainwave-frequencies are linked to differentemotional or mental states in the brain.

The EEG pattern may be derived from another individual or individuals,the same individual at a different time, or an in vivo animal model ofthe desired metal state. The method may therefore replicate a mentalstate of a first subject in a second subject. The mental state typicallyis not a state of consciousness or an idea, but rather a subconscious(in a technical sense) state, representing an emotion, readiness,receptivity, or other state, often independent of particular thoughts orideas. In essence, a mental state of the first subject (a “trainer” or“donor” who is in a desired mental state) is captured by recordingneural correlates of the mental state, e.g., as expressed by brainactivity patterns, such as EEG or MEG signals. The neural correlates ofthe first subject, either as direct or recorded representations, maythen be used to control a stimulation of the second subject (a “trainee”or “recipient”), seeking to induce the same brain activity patterns inthe second subject (recipient/trainee) as were present in the firstsubject (donor/trainer) to assist the second subject (recipient/trainee)to attain the desired mental state that had been attained by thedonor/trainer. In an alternative embodiment, the signals from the firstsubject (donor/trainer) being in the first mental state are employed toprevent the second subject (recipient/trainee) from achieving a secondmental state, wherein the second mental state is an undesirable one.

The source brain wave pattern may be acquired through multichannel EEGor MEG, from a human in the desired brain state. A computational modelof the brain state is difficult to create. However, such a model is notrequired according to the present technology. Rather, the signals may beprocessed by a statistical process (e.g., PCA or a related technology),or a statistically trained process (e.g., a neural network). Theprocessed signals preferably retain information regarding signal sourcespecial location, frequency, and phase. In stimulating the recipientsbrain, the source may be modified to account for brain size differences,electrode locations, etc. Therefore, the preserved characteristics arenormalized spatial characteristics, frequency, phase, and modulationpatterns.

The normalization may be based on feedback from the target subject, forexample based on a comparison of a present state of the target subjectand a corresponding state of the source subject, or other comparison ofknown states between the target and source. Typically, the excitationelectrodes in the target subject do not correspond to the feedbackelectrodes or the electrodes on the source subject Therefore, anadditional type of normalization is required, which may also be based ona statistical or statistically trained algorithm.

According to one embodiment, the stimulation of the second subject isassociated with a feedback process, to verify that the second subjecthas appropriately responded to the stimulation, e.g., has a predefinedsimilarity to the mental state as the first subject, has a mental statewith a predefined difference from the first subject, or has a desirechange from a baseline mental state. Advantageously, the stimulation maybe adaptive to the feedback. In some cases, the feedback may befunctional, i.e., not based on brain activity per se, or neuralcorrelates of mental state, but rather physical, psychological, orbehavioral effects that may be reported or observed.

The feedback typically is provided to a computational model-basedcontroller for the stimulator, which alters stimulation parameters tooptimize the stimulation in dependence on a brain and brain state modelapplicable to the target.

For example, it is believed that brainwaves represent a form ofresonance, where ensembles of neurons interact in a coordinated fashionas a set of coupled or interacting oscillators. The frequency of thewave is related to neural responsivity to neurotransmitters, distancesalong neural pathways, diffusion limitations, etc., and perhapspacemaker neurons or neural pathways. That is, the same mental state maybe represented by different frequencies in two different individuals,based on differences in the size of their brains, neuromodulatorspresent physiological differences, etc. These differences may bemeasured in microseconds or less, resulting in fractional changes infrequency. However, if the stimulus is different from the natural orresonant frequency of the target process, the result may be differentfrom that expected. Therefore, the model-based controller can determinethe parameters of neural transmission and ensemble characteristics,vis-à-vis stimulation, and resynthesize the stimulus wave to match thecorrect waveform, with the optimization of the waveform adaptivelydetermined. This may not be as simple as speeding up or slowing downplayback of the signal, as different elements of the various waveformsrepresenting neural correlates of mental state may have differentrelative differences between subjects. Therefore, according to one setof embodiments, the stimulator autocalibrates for the target based on acorrespondence (error) of a measured response to the stimulation and thedesired mental state sought by the stimulation. In cases where theresults are chaotic or unpredictable based on existing data, a geneticalgorithm may be employed to explore the range of stimulationparameters, and determine the response of the target. In some cases, thetarget has an abnormal or unexpected response to stimulation based on amodel maintained within the system. In this case, when the deviance fromthe expected response is identified, the system may seek to new model,such as from a model repository that may be on-line, such as through theInternet. If the models are predictable, a translation may be providedbetween an applicable model of a source or trainer, and the applicablemodel of the target to account for differences. In some cases, thedesired mental state is relatively universal, such as sleep and awake.In this case, the brain response model may be a statistical model,rather than a neural network or deep neural network type implementation.

Thus, in one embodiment, a hybrid approach is provided, with use ofdonor-derived brainwaves, on one hand, which may be extracted from thebrain activity readings (e.g., EEG or MEG) of the first at least onesubject (donor), preferably processed by principal component analysis,or spatial principal component analysis, autocorrelation, or otherstatistical processing technique (clustering, PCA, etc.) orstatistically trained technique (backpropagation of errors, etc.) thatseparates components of brain activity, which can then be modified ormodulated based on high-level parameters, e.g., abstractions. See,ml4a.github.io/ml4a/how_neural_networks_are_trained/. Thus, thestimulator may be programmed to induce a series of brain states definedby name (e.g., emotional or mental state 1, emotional or mental state 2,etc.) or as a sequence of “abstract” semantic labels, icons, or otherrepresentations, each corresponding to a technical brain state orsequence of sub-states. The sequence may be automatically defined, basedon biology and the system training, and thus relieve the programmer oflow-level tasks. However, in a general case, the present technologymaintains use of components or subcomponents of the donor's brainactivity readings, e.g., EEG or MEG, and does not seek to characterizeor abstract them to a semantic level.

According to the present technology, a neural network system orstatistical classifier may be employed to characterize the brain waveactivity and/or other data from a subject In addition to theclassification or abstraction, a reliability parameter is presented,which predicts the accuracy of the output Where the accuracy is high, amodel-based stimulator may be provided to select and/or parameterize themodel, and generate a stimulus for a target subject Where the accuracyis low, a filtered representation of the signal may be used to controlthe stimulator, bypassing the model(s). The advantage of this hybridscheme is that when the model-based stimulator is employed, manydifferent parameters may be explicitly controlled independent of thesource subject. On the other hand, where the data processing fails toyield a highly useful prediction of the correct model-based stimulatorparameters, the model itself may be avoided, in favor of a directstimulation type system.

Of course, in some cases, one or more components of the stimulation ofthe target subject may be represented as abstract or semanticallydefined signals, and more generally the processing of the signals todefine the stimulation will involve high level modulation ortransformation between the source signal received from the firstsubject, to define the target signal for stimulation of the secondsubject.

Preferably, each component represents a subset of the neural correlatesreflecting brain activity that have a high spatial autocorrelation inspace and time, or in a hybrid representation such as wavelet. Forexample, one signal may represent a modulated 10.2 Hz signal, whileanother signal represents a superposed modulated 15.7 Hz signal, withrespectively different spatial origins. These may be separated byoptimal filtering, once the spatial and temporal characteristics of thesignal are known, and bearing in mind that the signal is accompanied bya modulation pattern, and that the two components themselves may havesome weak coupling and interaction.

In some cases, the base frequency, modulation, coupling, noise, phasejitter, or other characteristic of the signal may be substituted. Forexample, if the first subject is listening to music, there will besignificant components of the neural correlates that are synchronizedwith the particular music. On the other hand, the music per se may notbe part of the desired stimulation of the target subject Therefore,through signal analysis and decomposition, the components of the signalfrom the first subject, which have a high temporal correlation with themusic, may be extracted or suppressed from the resulting signal.Further, the target subject may be in a different acoustic environment,and i may be appropriate to modify the residual signal dependent on theacoustic environment of the target subject, so that the stimulation isappropriate for achieving the desired effect, and does not representphantoms, distractions, or irrelevant or inappropriate content In orderto perform processing, it is convenient to store the signals or apartially processed representation, though a complete real-time signalprocessing chain may be implemented. Such a real-time signal processingchain is generally characterized in that the average size of a bufferremains constant, i.e., the lag between output and input is relativelyconstant, bearing in mind that there may be periodicity to theprocessing.

The mental state of the first subject may be identified, and the neuralcorrelates of brain activity captured. The second subject is subject tostimulation based on the captured neural correlates and the identifiedmental state. The mental state may be represented as a semanticvariable, within a limited classification space. The mental stateidentification need not be through analysis of the neural correlatessignal, and may be a volitional self-identification by the firstsubject, a manual classification by third parties, or an automateddetermination. The identified mental state is useful, for example,because it represents a target toward (or against) which the secondsubject can be steered.

The stimulation may be one or more inputs to the second subject, whichmay be an electrical or magnetic transcranial stimulation, sensorsstimulation, mechanical stimulation, ultrasonic stimulation, etc., andcontrolled with respect to waveform, intensity/amplitude, duration,feedback, self-reported effect by the second subject, manualclassification by third parties, automated analysis of brain activity,behavior, physiological parameters, etc. of the second subject.

The process may be used to induce in the target subject neuralcorrelates of the desired mental state, which are derived from adifferent time for the same person, or a different person at the same ora different time. For example, one seeks to induce the neural correlatesof the first subject in a desired mental state in a second subject,through the use of stimulation parameters comprising a waveform over aperiod of time derived from the neural correlates of mental state of thefirst subject.

The first and second subjects may be spatially remote from each other,and may be temporally remote as well. In some cases, the first andsecond subject are the same animal (e.g., human), temporally displaced.In other cases, the first and second subject are spatially proximate toeach other. In some cases, neural correlates of a desired mental stateare derived from a mammal having a simpler brain, which are thenextrapolated to a human brain. (Animal brain stimulation is alsopossible, for example to enhance training and performance). When thefirst and second subjects share a common environment, the signalprocessing of the neural correlates, and especially of real-timefeedback of neural correlates from the second subject may involveinteractive algorithms with the neural correlates of the first subject.

The first and second subjects may each be subject to stimulators. Thefirst subject and the second subject may communicate with each other inreal-time, with the first subject receiving stimulation based on thesecond subject, and the second subject receiving feedback based on thefirst subject. This can lead to synchronization of mental state betweenthe two subjects. However, the first subject need not receivestimulation based on real-time signals from the second subject, as thestimulation may derive from a third subject, or the first or secondsubjects at different points in time.

The neural correlates may be, for example, EEG, qEEG, or MEG signals.Traditionally, these signals are found to have dominant frequencies,which may be determined by various analyses. One embodiment providesthat the modulation pattern of a brainwave of the first subject isdetermined independent of the dominant frequency of the brainwave(though typically within the same class of brainwaves), and thismodulation imposed on a wave corresponding to the dominant frequency ofthe second subject That is, once the second subject achieves that samebrainwave pattern as the first subject (which may be achieved by meansother than electromagnetic, mechanical, or sensors stimulation), themodulation pattern of the first subject is imposed as a way of guidingthe mental state of the second subject.

The second subject may be stimulated with a stimulation signal whichfaithfully represents the frequency composition of a defined componentof the neural correlates of the first subject.

The stimulation may be performed, for example, by using a tDCS device, ahigh-definition tDCS device, a tACS device, a TMS device, a deep TMSdevice, and a source of one of a light signal and a sound signalconfigured to modulate the dominant frequency on the one of a lightsignal and a sound signal. The stimulus may be at least one of a lightsignal, a sound signal, an electric signal, and a magnetic field. Theelectric signal may be a direct current signal or an alternating currentsignal. The stimulus may be a transcranial electric stimulation, atranscranial magnetic stimulation, a deep magnetic stimulation, a lightstimulation, or a sound stimulation. A visual stimulus may be ambientlight or a direct light. An auditory stimulus may be binaural beats orisochronic tones.

The technology may also provide a processor configured to process theneural correlates of mental state from the first subject, and to produceor define a stimulation pattern for the second subject selectivelydependent on a waveform pattern of the neural correlates from the firstsubject Typically, the processor performs signal analysis and calculatesat least a dominant frequency of the brainwaves of the first subject,and preferably also spatial and phase patterns within the brain of thefirst subject.

A signal is presented to a second apparatus, configured to stimulate thesecond subject, which may be an open loop stimulation dependent on anon-feedback controlled algorithm, or a closed loop feedback dependentalgorithm. In other cases, analog processing is employed in part or inwhole, wherein the algorithm comprises an analog signal processingchain. The second apparatus receives information from the processor(first apparatus), typically comprising a representation of a portion ofa waveform represented in the neural correlates. The second apparatusproduces a stimulation intended to induce in the second subject thedesired mental state, e.g., representing the same mental state as waspresent in the first subject.

A typical process performed on the neural correlates is a filtering toremove noise. For example, notch filters may be provided at 50 Hz, 60Hz, 100 Hz, 120 Hz, and additional overtones. Other environmentalsignals may also be filtered in a frequency-selective orwaveform-selective (temporal) manner. Higher level filtering may also beemployed, as is known in the art. The neural correlates, after noisefiltering, may be encoded, compressed (lossy or losslessly), encrypted,or otherwise processed or transformed. The stimulator associated withthe second subject would typically perform decoding, decompression,decryption, inverse transformation, etc.

Information security and copy protection technology, similar to thatemployed for audio signals, may be employed to protect the neuralcorrelate signals from copying or content analysis before use. In somecases, it is possible to use the stored encrypted signal in itsencrypted for, without decryption. For example, with an asymmetricencryption scheme, which supports distance determination. See U.S. Pat.No. 7,269,277; Sahai and Waters (2005) Annual International Conferenceon the Theory and Applications of Cryptographic Techniques, pp. 457-473.Springer, Berlin, Heidelberg; Bringer et al. (2009) IEEE InternationalConference on Communications, pp. 1-6; Juels and Sudan (2006) Designs,Codes and Cryptography 2:237-257; Thaker et al. (2006) IEEEInternational Conference on Workload Characterization, pp. 142-149;Galil et al. (1987) Conference on the Theory and Application ofCryptographic Techniques, pp. 135-155.

Because the system may act intrusively, it may be desirable toauthenticate the stimulator or parameters employed by the stimulatorbefore use. For example, the stimulator and parameters it employs may beauthenticated by a distributed ledger, e.g., a blockchain. On the otherhand, in a closed system, digital signatures and other hierarchicalauthentication schemes may be employed. Permissions to perform certainprocesses may be defined according to smart contracts, which automatedpermissions (i.e., cryptographic authorization) provided from ablockchain or distributed ledger system. Of course, centralizedmanagement may also be employed.

In practice, the feedback signal from the second subject may becorrespondingly encoded as per the source signal, and the error betweenthe two minimized. In such an algorithm, the signal sought to beauthenticated is typically brought within an error tolerance of theencrypted signal before usable feedback is available. One way toaccomplish this is to provide a predetermined range of acceptableauthenticatable signals which are then encoded, such that anauthentication occurs when the putative signal matches any of thepredetermined range. In the case of the neural correlates, a large setof digital hash patterns may be provided representing different signalsas hash patterns. The net result is relatively weakened encryption, butthe cryptographic strength may still be sufficiently high to abate therisks.

The processor may perform a noise reduction distinct from afrequency-band filtering. The neural correlates may be transformed intoa sparse matrix, and in the transform domain, components representinghigh probability noise are masked, while components representing highprobability signal are preserved. The distinction may be optimized oradaptive. That is, in some cases, the components which representmodulation that are important may not be known a priori. However,dependent on their effect in inducing the desired response in the secondsubject, the ‘important’ components may be identified, and the remainderfiltered or suppressed. The transformed signal may then beinverse-transformed, and used as a basis for a stimulation signal.

A mental state modification, e.g., brain entrainment, may be provided,which ascertains a mental state in a plurality of first subjects;acquires brainwaves of the plurality of first subjects, e.g., using oneof EEG and MEG, to create a dataset containing representing brainwavesof the plurality of first subjects. The database may be encoded with aclassification of mental state, activities, environment, or stimuluspatterns, applied to the plurality of first subjects, and the databasemay include acquired brainwaves across a large number of mental states,activities, environment, or stimulus patterns, for example. In manycases, the database records will reflect a characteristic or dominatefrequency of the respective brainwaves. As discussed above, the traineror first subject is a convenient source of the stimulation parameters,but is not the sole available source. The database may be accessedaccording to its indexing, e.g., mental states, activities, environment,or stimulus patterns, for example, and a stimulation pattern for asecond subject defined based on the database records of one or moresubjects.

The record(s) thus retrieved are used to define a stimulation patternfor the second subject. The selection of records, and their use, may bedependent on the second subject and/or feedback from the second subject.As a relatively trivial example, a female second subject could bestimulated principally dependent on records from female first subjects.Of course, a more nuanced approach is to process the entirety of thedatabase and stimulate the second subject based on a global brainwave-stimulus model, though this is not required, and also, theunderlying basis for the model may prove unreliable or inaccurate. Infact, it may be preferred to derive a stimulus waveform from only asingle first subject, in order to preserve micro-modulation aspects ofthe signal, which as discussed above have not been fully characterized.However, the selection of the first subject(s) need not be static, andcan change frequently. The selection of first subject records may bebased on population statistics of other users of the records (i.e.,collaborative filtering, i.e., whose response pattern do I correlatehighest with? etc.). The selection of first subject records may also bebased on feedback patterns from the second user.

The process of stimulation may seek to target a desired mental state inthe second subject, which is automatically or semi-automaticallydetermined of manually entered. That target then represents a part ofthe query against the database to select the desired record(s). Theselection of records may be a dynamic process, and reselection ofrecords may be feedback dependent.

The records may be used to define a modulation waveform of a synthesizedcarrier or set of carriers, and the process may include a frequencydomain multiplexed multi-subcarrier signal (which is not necessarilyorthogonal). A plurality of stimuli may be applied concurrently, throughthe suffered subchannels and/or though different stimulator electrodes,magnetic field generators, mechanical stimulators, sensory stimulators,etc. The stimuli for the different subchannels or modalities need not bederived from the same records.

The stimulus may be applied to achieve the desired mental state, e.g.,brain entrainment of the second subject with one or more first subjects.Brain entrainment is not the only possible outcome of this process. Ifthe plurality of first subjects are mutually entrained, then each willhave a corresponding brain wave pattern dependent on the basis ofbrainwave entrainment. This link between first subject may be helpful indetermining compatibility between a respective first subject and thesecond subject. For example, characteristic patterns in the entrainedbrainwaves may be determined, even for different target mental states,and the characteristic patterns correlated to find relatively closematches and to exclude relatively poor matches.

This technology may also provide a basis for a social network, datingsite, employment or vocational testing, or other interpersonalenvironments, wherein people may be matched with each other based onentrainment characteristics. For example, people who efficiently entrainwith each other may have better social relationships than those who donot Thus, rather than seeking to match people based on personalityprofiles, the match could be made based on an ability of each party toefficiently entrain the brainwave pattern of the other party. Thisenhances non-verbal communication, and assists in achievingcorresponding states during activities. This can be assessed bymonitoring neural responses of each individual to video, and also byproviding a test stimulation based on the other party's brainwavecorrelates of mental state, to see whether coupling is efficientlyachieved. On the other hand, the technology could be used to assist inentrainment when natural coupling is inefficient, or to block couplingwhere the coupling is undesirable. An example of the latter ishostility; when two people are entrained in a hostile environment,emotional escalation ensures. However, if the entrainment is attenuated,undesired escalation may be impeded.

As discussed above, the plurality of first subjects may have theirrespective brain wave patterns stored in association with separatedatabase records. However, they may also be combined into a more globalmodel. One such model is a neural network or deep neural network.Typically, such a network would have recurrent features. Data from aplurality of first subjects is used to train the neural network, whichis then accessed by inputting the target state and/or feedbackinformation, and which outputs a stimulation pattern or parameters forcontrolling a stimulator. When multiple first subjects form the basisfor the stimulation pattern, it is preferred that the neural networkoutput parameters of the stimulation, derived from and comprisingfeatures of the brain wave patterns or other neural correlates of mentalstate from the plurality of first subjects, which are then used tocontrol a stimulator which, for example, generates its own carrierwave(s) which are then modulated based on the output of the neuralnetwork. The neural network need not periodically retrieve records, andtherefore may operate in a more time-continuous manner, rather than themore segmented scheme of record-based control.

In any of the feedback dependent methods, the brainwave patterns orother neural correlates of mental state may be processed by a neuralnetwork, to produce an output that guides or controls the stimulation.The stimulation, is, for example, at least one of a light (visual)signal, a sound signal, an electric signal, a magnetic field, and avibration or mechanical stimulus, or other sensory input. The fields maybe static or dynamically varying.

The process may employ a relational database of mental states andbrainwave patterns, e.g., frequencies/neural correlate waveform patternsassociated with the respective mental states. The relational databasemay comprise a first table, the first table further comprising aplurality of data records of brainwave patterns, and a second table, thesecond table comprising a plurality of mental states, each of the mentalstates being linked to at least one brainwave pattern. Data related tomental states and brainwave patterns associated with the mental statesare stored in the relational database and maintained. The relationaldatabase is accessed by receiving queries for selected mental states,and data records are returned representing the associated brainwavepattern. The brainwave pattern retrieved from the relational databasemay then be used for modulating a stimulator seeking to produce aneffect selectively dependent on the mental state at issue.

A computer apparatus may be provided for creating and maintaining arelational database of mental states and frequencies associated with themental states, the computer apparatus comprising: a non-volatile memoryfor storing a relational database of mental states and neural correlatesof brain activity associated with the mental states, the databasecomprising a first table, the first table further comprising a pluralityof data records of neural correlates of brain activity associated withthe mental states, and a second table, the second table comprising aplurality of mental states, each of the mental states being linked toone or more records in the first table; a processor coupled with thenon-volatile memory, configured to process relational database queries,which are then used for searching the database; RAM coupled with theprocessor and the non-volatile memory for temporary holding databasequeries and data records retrieved from the relational database; and anI/O interface configured to receive database queries and deliver datarecords retrieved from the relational database. A SQL or noSQL databasemay also be used to store and retrieve records.

A further aspect of the technology provides a method of brainentrainment comprising: ascertaining a mental state in a first subject;recording brainwaves of the plurality of subjects using at least onechannel one of EEG and MEG; storing the recorded brainwaves in aphysical memory device; retrieving the brainwaves from the memorydevice; applying a stimulus signal comprising a brainwave patternderived from at least one-channel one of the EEG and MEG to a secondsubject via transcranial stimulation, whereby the mental state desiredby the second subject is achieved. The stimulation may be of the sameorder (number of channels) as the EEG or MEG, or a different number ofchannels, typically reduced. For example, the EEG or MEG may comprise128 or 256 channels, while the transcranial stimulator may have 8 orfewer channels. Sensory stimulation of various modalities and patternsmay accompany the transcranial stimulation.

The at least one channel may be less than six channels and the placementof electrodes used for transcranial stimulation may be approximately thesame as the placement of electrodes used in recording of said one of EEGand MEG.

The present technology may be responsive to chronobiology, and inparticular to the subjective sense of time. For a subject, this may bedetermined volitionally subjectively, but also automatically, forexample by judging attention span, using e.g., eye movements, andanalyzing persistence of brainwave patterns or other physiologicalparameters after a discrete stimulus. Further, time-constants of thebrain, reflected by delays and phase may also be analyzed. Further, thecontingent negative variation (CNV) preceding a volitional act may beused, both to determine (or measure) conscious action timing, and alsothe time relationships between thought and action more generally.

Typically, brainwave activity is measured with a large number of EEGelectrodes, which each receive signals from a small area on the scalp,or in the case of a MEG, by a number of sensitive magnetic fielddetectors, which are responsive to local field differences. Typically,the brainwave capture is performed in a relatively high number ofspatial dimensions, e.g., corresponding to the number of sensors. It isoften unfeasible to process the brainwave signals to create a sourcemodel, given that the brainwaves are created by billions of neurons,connected through axons, which have long distances. Further, the neuronsare generally non-linear, and interconnected. However, a source model isnot required.

Various types of artificial intelligence techniques may be exploited toanalyze the neural correlates of an emotional or mental staterepresented in the brain activity data of both the first subject (donor)(or plurality of donors) and the second subject (recipient). Thealgorithm or implementation need not be the same, though in some cases,it is useful to confirm the approach of the source processing andfeedback processing so that the feedback does not achieve or seek asuboptimal target emotional or mental state. However, given the possibledifferences in conditions, resources, equipment, and purpose, there isno necessary coordination of these processes. The artificialintelligence may take the form of neural networks or deep neuralnetworks, though rule/expert-based systems, hybrids, and more classicalstatistical analysis may be used. In a typical case, an artificialintelligence process will have at least one aspect, which is non-linearin its output response to an input signal, and thus at least theprinciple of linear superposition is violated. Such systems tend topermit discrimination, since a decision and the process ofdecision-making are, ultimately, non-linear. An artificially intelligentsystem requires a base of experience or information upon which to train.This can be a supervised (external labels applied to data), unsupervised(self-discrimination of classes), or semi-supervised (a portion of thedata is externally labelled).

A self-learning or genetic algorithm may be used to tune the system,including both or either the signal processing at the donor system andthe recipient system. In a genetic algorithm feedback-dependentself-learning system, the responsivity of a subject, e.g., the target,to various kinds of stimuli may be determined over a stimulus space.This stimulation may be in the context of use, with a specific targetemotional or mental state provided, or unconstrained. The stimulator mayoperate using a library of stimulus patterns, or seek to generatesynthetic patterns or modifications of patterns. Over a period of time,the system will learn to map a desired emotional or mental state tooptimal context-dependent parameters of the stimulus pattern.

In some cases it may be appropriate to administer a drug orpharmacological agent such as melatonin, hypnotic or soporific drug, asedative (e.g., barbiturates, benzodiazepines, nonbenzodiazepinehypnotics, orexin antagonists, antihistamines, general anesthetics,cannabis and other herbal sedatives, methaqualone and analogues, musclerelaxants, opioids) that assists in achieving the target emotional ormental state, and for emotional states and/or dreams, this may includecertain psychotropic drugs, such as epinephrine, norepinephrine reuptakeinhibitors, serotonin reuptake inhibitors, peptide endocrine hormones,such as oxytocin, ACTH fragments, insulin, etc. Combining a drug withstimulation may reduce the required dose of the drug and the associatedside effects of the drug.

The technology may be used to modify or alter a mental state (e.g., fromsleep to waking and vice versa) in a subject. Typically, the startingmental state, brain state, or brainwave pattern is assessed, such as byEEG, MEG, observation, stimulus-response amplitude and/or delay, or thelike. Of particular interest in uncontrolled environments are automatedmental state assessments, which do not rely on human observation or EEGsignals, and rather may be acquired through MEG (e.g., SQID,optically-pumped magnetometer), EMG, MM G (magnetomyogram), mechanical(e.g., accelerometer, gyroscope, etc.), data from physiological sensors(e.g., AKG, heartrate, respiration rate, temperature, galvanic skimpotential, etc.), or automated camera sensors.

For example, cortical stimulus-response pathways and reflexes may beexercised automatically, to determine their characteristics on agenerally continuous basis. These characteristics may include, forexample, a delay between stimulus and the observed central (e.g., EEG)or peripheral response (e.g., EMG, limb accelerometer, video).Typically, the same modality will be used to assess the pre-stimulationstate, stimulus response, and post-stimulation state, though this is nota limitation.

In order to change the mental state, a stimulus is applied in a waydesigned to alter the mental state in a desired manner. A statetransition table, or algorithm, may be employed to optimize thetransition from a starting mental state to a desired mental state. Thestimulus may be provided in an open loop (predetermined stimulusprotocol) or closed loop (feedback adapted stimulus protocol), based onobserved changes in a monitored variable.

Advantageously, a characteristic delay between application of stimulusand determination of response varies with the brain or mental state. Forexample, some mental states may lead to increased delay or greatervariability in delay, while others may lead to decreased or lowervariability. Further, some states may lead to attenuation of response,while others may lead to exaggerated response. In addition, differentmental states can be associated with qualitatively different responses.Typically, the mere assessment of the brain or mental state should notitself alter the state, though in some cases the assessment andtransition influence may be combined. For example, in seeking to assistin achieving a deep sleep state, excitation that disturbs sleep iscontraindicated.

In cases where a brainwave pattern is itself determined by EEG (whichmay be limited to relatively controlled environments), brainwavesrepresenting that pattern represent coherent firing of an ensemble ofneurons, defining a phase. One way to change the state is to advance orretard the triggering of the neuronal excitation, which can be a director indirect excitation or inhibition, caused, for example, byelectrical, magnetic, mechanical, or sensory stimulation. Thisstimulation may be time-synchronized with the detected (e.g., by EEG)brainwaves, for example with a phase lead or lag with respect to thedetected pattern. Further, the excitation can steer the brainwave signalby continually advancing to a desired state, which through the continualphase rotation represents a different frequency. After the desired newstate is achieved, the stimulus may cease, or be maintained in aphase-locked manner to hold the desired state.

A predictive model may be used to determine the current mental state,optimal transition to a desired mental state, when the subject hasachieved the desired mental state, and how to maintain the desiredmental state. The desired mental state itself may represent a dynamicsequence (e.g., stage 1→stage 2→stage 3, etc.), such that the subjectsmental state is held for a desired period in a defined condition.Accordingly, the stimulus may be time-synchronized with respect to themeasured brainwave pattern.

Direct measurement or determination of brainwaves or their phaserelationships is not necessarily required. Rather, the system maydetermine tremor or reflex patterns. Typically, the reflex patterns ofinterest involve central pathways, and more preferably brain reflexpathways, and not spinal cord mediated reflexes, which are lessdependent on instantaneous brain state. The central reflex patterns canreflect a time delay between stimulation and motor response, anamplitude of motor response, a distribution of response through variousafferent pathways, variability of response, tremor or other modulationof motor activity, etc. Combinations of these characteristics may beemployed, and different subsets may be employed at different times or toreflect different states. Similar to evoked potentials, the stimulus maybe any sense, especially sight, sound, touch/proprioception/pain/etc.,though the other senses, such as taste, smell, balance, etc., may alsobe exercised. A direct electrical or magnetic excitation is alsopossible. As discussed, the response may be determined through EEG, MEG,or peripheral afferent pathways.

Normalization of brain activity information may be spatial and/ortemporal. For example, the EEG electrodes between sessions or fordifferent subject may be in different locations, leading to a distortionof the multichannel spatial arrangement. Further, head size and shape ofdifferent individuals is different, and this needs to be normalizedand/or encoded as well. The size and shape of the head/skull and/orbrain, may also lead to temporal differences in the signals, such ascharacteristic time delays, resonant or characteristic frequencies, etc.

One way to account for these effects is through use of a time-spacetransform, such as a wavelet-type transform. It is noted that, in acorresponding way that statistical processes are subject to frequencydecomposition analysis through Fourier transforms, they are also subjectto time-frequency decomposition through wavelet transforms. Typically,the wavelet transform is a discrete wavelet transform (DWT), though morecomplex and less regular transforms may be employed. As discussed above,principal component analysis (PCA) and spatial PCA may be used toanalyze signals, presuming linearity (linear superposition) andstatistical independence of components. However, these presumptionstechnically do not apply to brainwave data, and practically, one wouldnormally expect interaction between brain wave components(non-independence) and lack of linearity (since “neural networks” bytheir nature are non-linear), defeating use of PCA or spatial PCAunmodified. However, a field of nonlinear dimensionality reductionprovides various techniques to permit corresponding analyses underpresumptions of non-linearity and non-independence. See:

-   en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction,-   www.image.ucar.edu/pub/toyIV/monahan_5_16.pdf (An Introduction to    Nonlinear Principal Component Analysis, Adam Monahan),-   Barros, Allan Kardec, and Andrzej Cichocki. “Extraction of specific    signals with temporal structure.” Neural computation 13, no. 9    (2001): 1995-2003;-   Ewald, Ame. “Novel multivariate data analysis techniques to    determine functionally connected networks within the brain from EEG    or MEG data.” (2014);-   Friston, Karl J. “Basic concepts and overview.” SPMcourse, Short    course; Crainiceanu, Ciprian M., Ana-Maria Staicu, Shubankar Ray,    and Naresh Punjabi. “Statistical inference on the difference in the    means of two correlated functional processes: an application to    sleep EEG power spectra.” Johns Hopkins University, Dept of    Biostatistics Working Papers (2011): 225;-   Friston, Karl J., Andrew P. Holmes, Keith J. Worsley, J-P. Poline,    Chris D. Frith, and Richard S J Frackowiak “Statistical parametric    maps in functional imaging: a general linear approach.” Human brain    mapping 2, no. 4 (1994): 189-210;-   Howard et al., “Distinct Variation Pattern Discovery Using    Altemating Nonlinear Principal Component Analysis”, IEEE Trans    Neural Network Leam Syst 2018 January; 29(1):156-166. doi:    10.1109/TNNLS.2016.2616145. Epub 2016 Oct. 26    (www.ncbi.nlm.nih.gov/pubmed/27810837);-   Hyvärinen, Aapo, and Patrik Hoyer. “Emergence of phase- and    shift-invariant features by decomposition of natural images into    independent feature subspaces.” Neural computation 12, no. 7 (2000):    1705-1720;-   Jolliffe, I. T., “Principal Component Analysis, Second Edition”,    Springer 2002, cda.psych.uiuc.edu/staistcal_eaming_course/Jollffe    %201.%20Principal %20Component %20Analysis %20(2ed., Springer,    2002)(518s)_MVsa_.pdf,-   Jutten, Christian, and Massoud Babaie-Zadeh. “Source separation:    Principles, current advances and applications.” IAR Annu Meet Nancy    Fr 110 (2006);-   Karl Friston, “Nonlinear PCA: characterizing interactions between    modes of brain activity” (www.fil.ion.ucl.ac.uk/-kar/Nonlinear    %2PCA.pdf, 2000),-   Konar, Amit, and Aruna Chakraborty. Emotion recognition: A pattern    analysis approach. John Wiley & Sons, 2014; Kohl, Florian. “Blind    separation of dependent source signals for MEG sensory stimulation    experiments.” (2013);-   Lee, Soo-Young. “Blind source separation and independent component    analysis: A review.” Neural Information Processing-Letters and    Reviews 6, no. 1 (2005): 1-57;-   Nonlinear PCA    (www.comp.nus.edu.sg/-cs5240/lecture/nonlinear-pca.pdf),-   Nonlinear PCA toolbox for MATLAB (www.nlpca.org),-   Nonlinear Principal Component Analysis: Neural Network Models and    Applications    (pdfs.semanticscholar.org/9d31/23542031a227d2f4c4602066cf8ebceaeb7a.pdf),-   Nonlinear Principal Components Analysis: Introduction and    Application    (openaccess.leidenuniv.nl/bitstrearm/handle/1887/12386/Chapter2.pdf?sequence=10,    2007),-   Onken, Amo, Jian K. Liu, P P Chamanthi R. Karunasekara, loannis    Delis, Tim Gollisch, and Stefano Panzeri. “Using matrix and tensor    factorizations for the single-trial analysis of population spike    trains.” PLoS computational biology 12, no. 11 (2016): e1005189;-   Parida, Shantipriya, Satchidananda Dehuri, and Sung-Bae Cho.    “Machine Learning Approaches for Cognitive State Classification and    Brain Activity Prediction: A Survey.” Current Bioinformatics 10, no.    4 (2015): 344-359;-   Saproo, Sameer, Victor Shih, David C. Jangraw, and Paul Sajda.    “Neural mechanisms underlying catastrophic failure in human-machine    interaction during aerial navigation.” Journal of neural engineering    13, no. 6 (2016): 066005;-   Stone, James V. “Blind source separation using temporal    predictability.” Neural computation 13, no. 7 (2001): 1559-1574;-   Tressoldi, Patrizio, Luciano Pederzoli, Marco Bilucaglia, Patrizio    Caini, Pasquale Fedele, Alessandro Fenini, Simone Melloni, Diana    Richeldi, Florentina Richeldi, and Agostino Accardo. “Brain-to-Brain    (Mind-to-Mind) Interaction at Distance: A Confirmatory Study.”    (2014).    f1000researchdata.s3.amazonaws.com/manuscripts/5914/5adbf847-787a-4fc1-ac04-2e1cd61ca972_4336-_patizio_tressoldi_v3.pdf?doi=10.12688/f1000research.4336.3;-   Tsiaparas, Nikolaos N. “Wavelet analysis in coherence estimation of    electroencephalographic signals in children for the detection of    dyslexia-related abnormalities.” PhD diss., 2006.-   Valente, Giancarlo. “Separazione cieca di sorgenti in ambienti    reali: nuovi algoritmi, applicazioni e implementazioni.” (2006);    SAPIENZA, L A. “Blind Source Separation in real-world environments:    new algorithms, applications and implementations Separazione cieca    di sorgenti in ambienti reali: nuovi algoritmi, applicazioni e.”;-   Wahlund, Bjöm, Wlodzimierz Klonowski, Pawet Stepien, Robert Stepien,    Tatjana von Rosen, and Dietrich von Rosen. “EEG data, fractal    dimension and multivariate statistics.” Journal of Computer Science    and Engineering 3, no. 1 (2010): 10-14;-   Wang, Yan, Mat thew T. Sutherland, Lori L. Sanfratello, and    Akaysha C. Tang. “Single-trial classification of ERPS using    second-order blind identification (SOBI).” In Machine Leaning and    Cybemetics, 2004. Proceedings of 2004 International Conference on,    vol. 7, pp. 4246-4251. IEEE, 2004;-   Yu, Xianchuan, Dan Hu, and Jindong Xu. Blind source separation:    theory and applications. John Wiley & Sons, 2013.

Therefore, statistical approaches are available for separating EEGsignals from other signals, and for analyzing components of EEG signalsthemselves. According to the present invention, various components thatmight be considered noise in other contexts, e.g., according to priortechnologies, such as a modulation pattern of a brainwave, arepreserved. Likewise, interactions and characteristic delays betweensignificant brainwave events are preserved. This information may bestored either integrated with the brainwave pattern in which it occurs,or as a separated modulation pattern that can then be recombined with anunmodulated brainwave pattern to approximate the original subject.

According to the present technology, lossy “perceptual” encoding (i.e.,functionally optimized with respect to subjective response) of thebrainwaves may be employed to process, store and communicate thebrainwave information. In a testing scenario, the “perceptual” featuresmay be tested, so that important information is preserved overinformation that does not strongly correspond to the effective signal.Thus, while one might not know a priori which components representuseful information, a genetic algorithm may empirically determine whichfeatures or data reduction algorithms or parameter sets optimizeretention of useful information vs. information efficiency. It is notedthat subjects may differ in their response to signal components, andtherefore the “perceptual” encoding may be subjective with respect tothe recipient. On the other hand, different donors may have differentinformation patterns, and therefore each donor may also requireindividual processing. As a result, pairs of donor and recipient mayrequire optimization, to ensure accurate and efficient communication ofthe relevant information. According to the present invention, sleep/wakemental states and their corresponding patterns are sought to betransferred. In the recipient, these patterns have characteristicbrainwave patterns. Thus, the donor may be used, under a variety ofalternate processing schemes, to stimulate the recipient, and thesleep/wake response of the recipient determined based on objectivecriteria, such as resulting brainwave patterns or expert observerreports, or subjective criteria, such as recipient self-reporting,survey or feedback. Thus, after a training period, an optimizedprocessing of the donor, which may include filtering, dominant frequencyresynthesis, feature extraction, etc., may be employed, which isoptimized for both donor and recipient In other cases, the donorcharacteristics may be sufficiently normalized, that only recipientcharacteristics need be compensated. In a trivial case, there is onlyone exemplar donor, and the signal is oversampled and losslesslyrecorded, leaving only recipient variation as a significant factor.

Because dominant frequencies tend to have low information content (ascompared to the modulation of these frequencies and interrelation ofvarious sources within the brain), one efficient way to encode the mainfrequencies is by location, frequency, phase, and amplitude. Themodulation of a wave may also be represented as a set of parameters. Bydecomposing the brainwaves according to functional attributes, i becomespossible, during stimulation, to modify the sequence of “events” fromthe donor, so that the recipient need not experience the same events, inthe same order, and in the same duration, as the donor. Rather, ahigh-level control may select states, dwell times, and transitionsbetween states, based on classified patterns of the donor brainwaves.The extraction and analysis of the brainwaves of the donors, andresponse of the recipient, may be performed using statistical processes,such as principle components analysis (PCA), independent componentanalysis (ICA), and related techniques; clustering, classification,dimensionality reduction and related techniques; neural networks andother known technologies. These algorithms may be implemented on generalpurpose CPUs, array processors such as GPUs, and other technologies.

In practice, a brainwave pattern of the first subject may be analyzed bya PCA technique that respects the non-linearity and non-independence ofthe brainwave signals, to extract the major cyclic components, theirrespective modulation patterns, and their respective interrelation. Themajor cyclic components may be resynthesized by a waveform synthesizer,and thus may be efficiently coded. Further, a waveform synthesizer maymodify frequencies or relationships of components from the donor basedon normalization and recipient characteristic parameters. For example,the brain of the second subject (recipient) may have characteristicclassified brainwave frequencies 3% lower than the donor (or each typeof wave may be separately parameterized), and therefore the resynthesismay take this difference into account. The modulation patterns andinterrelations may then be reimposed onto the resynthesized patterns.The normalization of the modulation patterns and interrelations may bedistinct from the underlying major cyclic components, and thiscorrection may also be made, and the normalized modulation patterns andinterrelations included in the resynthesis. If the temporalmodifications are not equal, the modulation patterns and interrelationsmay be decimated or interpolated to provide a correct continuous timesequence of the stimulator. The stimulator may include one or morestimulation channels, which may be implemented as electrical, magnetic,auditory, visual, tactile, or other stimulus, and/or combinations.

The stimulator is preferably feedback controlled. The feedback mayrelate to the brainwave pattern of the recipient, and/or context orancillary biometric basis. For example, if the second subject(recipient) begins to awaken from sleep, which differs from the firstsubject (donor) sleep pattern, then the stimulator may resynchronizebased on this finding. That is, the stimulator control will enter a modecorresponding to the actual state of the recipient and seek to guide therecipient to a desired state from a current state, using the availablerange and set of stimulation parameters. The feedback may also be usedto tune the stimulator, to minimize error from a predicted or desiredstate of the recipient subject based on the prior and currentstimulation.

The control for the stimulator is preferably adaptive, and may employ agenetic algorithm to improve performance over time. For example, ifthere are multiple first subjects (donors), the second subject(recipient) may be matched with those donors from whose brainwavesignals (or algorithmically modified versions thereof) the predictedresponse in the recipient is best, and distinguished from those donorsfrom whose brainwave signals the predicted response in the recipientsubject poorly corresponds. Similarly, if the donors have brainwavepatterns determined over a range of time and context and stored in adatabase, the selection of alternates from the database may be optimizedto ensure best correspondence of the recipient subject to the desiredresponse.

It is noted that a resynthesizer-based stimulator is not required, if asignal pattern from a donor is available that properly corresponds tothe recipient and permits a sufficiently low error between the desiredresponse and the actual response. For example, if a donor and arecipient are the same subject at different times, a large database maybe unnecessary, and the stimulation signal may be a minimally processedrecording of the same subject at an earlier time. Likewise, in somecases, a deviation is tolerable, and an exemplar signal may be emitted,with relatively slow periodic correction. For example, a sleep signalmay be derived from a single subject, and replayed with a periodicity of90 minutes or 180 minutes, such as a light or sound signal, which may beuseful in a dormitory setting, where individual feedback is unavailableor unhelpful.

In some cases, i is useful to provide a stimulator and feedback-basedcontroller on the donor. This will better match the conditions of thedonor and recipient, and further allow determination of not only thebrainwave pattern of the donor, but also responsivity of the donor tothe feedback. One difference between the donors and the recipients isthat in the donor, the natural sleep pattern is sought to be maintainedand not interrupted. Thus, the adaptive multi-subject database mayinclude data records from all subject, whether selected ab initio as auseful exemplar or not Therefore, the issue is whether a predictable anduseful response can be induced in the recipient from the databaserecord, and if so, that record may be employed. If the record wouldproduce an unpredictable result, or a non-useful result, the use of thatrecord should be avoided. The predictability and usefulness of theresponses may be determined by a genetic algorithm, or otherparameter-space searching technology.

FIG. 1 shows the electric activity of a neuron contributing to abrainwave.

FIG. 2 shows transmission of an electrical signal generated by a neuronthrough the skull, skin and other tissue to be detectable by anelectrode transmitting this signal to EEG amplifier.

FIG. 3 shows an illustration of a typical EEG setup with a subjectwearing a cup with electrodes connected to the EEG machine, which is, inturn, connected to a computer screen displaying the EEG. FIG. 4 shows atypical EEG reading. FIG. 5 shows one second of a typical EEG signal.FIG. 6 shows main brainwave patterns.

FIG. 7 shows a flowchart according to one embodiment of the invention.Brainwaves from a subject who is in an emotional state are recorded.Brainwaves associated with the emotion are identified. A temporalpattern in the brainwave associated with the emotion is decoded. Thedecoded temporal pattern is used to modulate the frequency of at leastone stimulus. The temporal pattern is transmitted to the second subjectby exposing the second subject to said at least one stimulus.

FIG. 8 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject at rest and in an emotional state are recorded,and a brainwave characteristic associated with the emotion is separatedby comparing with the brainwaves at rest A temporal pattern in thebrainwave associated with the emotion is decoded and stored. The storedcode is used to modulate the temporal pattern on a stimulus, which istransmitted to the second subject by exposing the second subject to thestimulus

FIG. 9 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject in an emotional state are recorded, and aFourier Transform analysis performed. A temporal pattern in thebrainwave associated with the emotion is then decoded and stored. Thestored code is then used to modulate the temporal pattern on a stimulus,which is transmitted to the second subject by exposing the secondsubject to the stimulus.

FIG. 10 shows a flowchart according to one embodiment of the invention.Brainwaves in a plurality of subjects in a respective emotional stateare recorded. A neural network is trained on the recorded brainwavesassociated with the emotion. After the neural network is defined,brainwaves in a first subject engaged in the emotion are recorded. Theneural network is used to recognize brainwaves associated with theemotion. A temporal pattern in the brainwaves associated with theemotion is decoded and stored. The code is used to modulate the temporalpattern on a stimulus. Brainwaves associated with the emotion in asecond subject are induced by exposing the second subject to thestimulus

FIG. 11 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject both at rest and in an emotional state arerecorded. A brainwave pattern associated with the emotion is separatedby comparing with the brainwaves at rest. For example, a filter oroptimal filter may be designed to distinguish between the patterns. Atemporal pattern in the brainwave associated with the emotion isdecoded, and stored in software code, which is then used to modulate thetemporal pattern of light, which is transmitted to the second subject,by exposing the second subject to the source of the light FIG. 12 showsa flowchart according to one embodiment of the invention. Brainwaves ina subject at rest and in an emotion are recoded. A brainwave patternassociated with the emotion is separated by comparing with thebrainwaves at rest A temporal pattern in the brainwave associated withthe emotion is decoded and stored as a temporal pattern in softwarecode. The software code is used to modulate the temporal pattern on asound signal. The temporal pattern is transmitted to the second subjectby exposing the second subject to the sound signal.

FIG. 13 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject in an emotional state are recorded, andbrainwaves selectively associated with the emotion are identified. Apattern, e.g., a temporal pattern, in the brainwave associated with theemotion, is decoded and used to entrain the brainwaves of the secondsubject.

FIG. 14 shows a schematic representation of an apparatus according toone embodiment of the invention.

FIG. 15 shows brainwave real time BOLD (Blood Oxygen Level Dependent)fMRI studies acquired with synchronized stimuli.

FIG. 16 shows that a desired metal state may be induced in a targetindividual (e.g., human, animal), by providing selective stimulationaccording to a temporal pattern, wherein the temporal pattern iscorrelated with an EEG pattern of the target when in the desired mentalstate, or represents a transition which represents an intermediatetoward achieving the desired mental state. The temporal pattern may betargeted to a discrete spatial region within the brain, either by aphysical arrangement of a stimulator, or natural neural pathways throughwhich the stimulation (or its result) passes.

FIG. 17 shows brainwave entrainment before and after synchronization.See, Understanding Brainwaves to Expand our Consciousness,fractalenlightenment.com/14794/spidtuality/understanding-brainwaves-to-expand-our-consciousness

FIG. 18 shows brainwaves during inefficient problem solving and stress.

FIGS. 19 and 20 show how binaural beats work. Binaural beats areperceived when two different pure-tone sine waves, both with frequencieslower than 1500 Hz, with less than a 40 Hz difference between them, arepresented to a listener dichotically (one through each ear). See, forexample, if a 530 Hz pure tone is presented to a subject's right ear,while a 520 Hz pure tone is presented to the subjects left ear, thelistener will perceive the auditory illusion of a third tone, inaddition to the two pure-tones presented to each ear. The third sound iscalled a binaural beat, and in this example would have a perceived pitchcorrelating to a frequency of 10 Hz, that being the difference betweenthe 530 Hz and 520 Hz pure tones presented to each ear. Binaural-beatperception originates in the inferior colliculus of the midbrain and thesuperior olivary complex of the brainstem, where auditory signals fromeach ear are integrated and precipitate electrical impulses along neuralpathways through the reticular formation up the midbrain to thethalamus, auditory cortex, and other cortical regions.

FIG. 21 shows Functional Magnetic Resonance Imaging (fMRI)

FIG. 22 shows a photo of a brain forming a new idea.

FIG. 23 shows 3D T2 CUBE (SPACE/VISTA) FLAIR & DSI tractography.

FIG. 24 shows The EEG activities for a healthy subject during a workingmemory task.

FIG. 25 shows a flowchart according to one embodiment of the invention.Brainwaves in a subject in an emotional state are recorded. Brainwavesassociated with the emotion are identified. A temporal pattern in thebrainwave associated with the emotion is extracted. First and seconddynamic audio stimuli are generated, whose frequency differentialcorresponds to the temporal pattern. Binaural beats are provided usingthe first and the second audio stimuli to stereo headphones worn by thesecond subject to entrain the brainwaves of the second subject.

FIG. 25 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject engaged in an emotional state are recorded, andbrainwaves associated with the emotion identified. A pattern in thebrainwave associated with the emotion is identified, having a temporalvariation. Two dynamic audio stimuli whose frequency differentialcorresponds to the temporal variation are generated, and applied as aset of binaural bits to the second subject, to entrain the brainwaves ofthe second subject.

FIG. 26 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject in an emotional state are recorded, andbrainwaves associated with the emotion identified. A pattern in thebrainwave associated with the emotion is identified, having a temporalvariation. A series of isochronic tones whose frequency differentialcorresponds to the temporal variation is generated and applied as a setof stimuli to the second subject, to entrain the brainwaves of thesecond subject See:

FIG. 27 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject in an emotional state are recorded, andbrainwaves associated with the emotion identified. A pattern in thebrainwave associated with the emotion is identified, having a temporalvariation. Two dynamic light stimuli whose frequency differentialcorresponds to the temporal variation are generated, and applied as aset of stimuli to the second subject, wherein each eye sees only onelight stimulus, to entrain the brainwaves of the second subject.

FIG. 28 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject in an emotional state are recorded, andbrainwaves associated with the emotion identified. A pattern in thebrainwave associated with the emotion is identified, having a temporalvariation. Two dynamic electric stimuli whose frequency differentialcorresponds to the temporal variation are generated, and applied astranscranial stimulation to the second subject, wherein each electricsignal is applied to the opposite side of the subjects head, to entrainthe brainwaves of the second subject.

FIG. 29 shows a flowchart according to one embodiment of the invention.Brainwaves of a subject are recorded at rest, and in an emotional state.A brainwave associated with the emotion is separated from the remainderof the signal by comparison with the brainwaves at rest A temporalpattern if the brainwave associated with the emotion is decoded, andstored in software code, in a memory. The software code is then used tomodulate a temporal pattern in light, which is transmitted to a secondsubject, who is exposed to the light.

FIG. 30 shows picture of brain anatomy. FIG. 31 shows a brain map. FIG.32 shows an image depicting neuron anatomy. FIG. 33 shows graphsrepresenting a dimensional view of emotions. FIG. 34 shows arepresentation of neural activity with respect to emotional state.

In one embodiment, as shown in FIG. 35, brainwaves of the first subject(donor) being in a positive emotional state are recorded 10. A temporaland spatial patterns are decoded from the recorded brainwaves 20 andstored in a non-volatile memory 30. At a later time, the temporal andspatial patters are retrieved from the non-volatile memory 40 andmodulated on at least one stimulus 50, which is applied to the firstsubject via non-invasive brain stimulation technique 60 to induce thepositive emotional state. The positive emotional state may be one of ora combination of the state of happiness, joy, gladness, cheerfulness,delight, optimism, merriment, jovialness, vivaciousness, pleasure,excitement, sexual arousal, exuberance, bliss, ecstasy, relaxation,harmony peacefulness.

In another embodiment, as shown in FIG. 36, brainwaves of the firstsubject being in a positive emotional state are recorded using EEG 80. Atemporal and spatial patterns are decoded from the EEG 70 and stored ina non-volatile memory 90. At a later lime, the temporal and spatialpatters are retrieved from the non-volatile memory 100 and modulated ona direct current 110, which is applied to the first subject viatranscranial direct current stimulation (tDCS) 120 to induce thepositive emotional state. See FIG. 42.

In further embodiment, as shown in FIG. 37, brainwaves of the firstsubject being in a positive emotional state are recorded using EEG 130.A temporal and spatial patterns are decoded from the EEG 140 and storedin a non-volatile memory 150. At a later time, the temporal and spatialpatters are retrieved from the non-volatile memory 160 and modulated onan alternating current 170, which is applied to the first subject viatranscranial alternating current stimulation (tACS) 180 to induce thepositive emotional state. It will be understood by a person skilled inthe art that transcranial pulsed current stimulation (tPCS),transcranial random noise stimulation (tRNS), or any other type oftranscranial electrical stimulation (tES) may be used. See FIGS. 43-47.

In certain embodiments, as shown in FIG. 38, brainwaves of the firstsubject being in a positive emotional state are recorded usingmagnetoencephalogram (MEG) 190. A temporal and spatial patterns aredecoded from the MEG 200 and stored in a non-volatile memory 210. At alater time, the temporal and spatial patters are retrieved from thenon-volatile memory 220 and modulated on a magnetic field 230, which isapplied to the second subject via transcranial magnetic stimulation(tMS) 240 to induce the positive emotional state.

In certain embodiments, as shown in FIG. 39, brainwaves of the firstsubject being in a positive emotional state are recorded usingelectroencephalogram (EEG) 250. A temporal and spatial patterns aredecoded from the EEG 260 and stored in a non-volatile memory 270. At alater time, the temporal and spatial patters are retrieved from thenon-volatile memory 280 and modulated on a light signal 290, which isprojected to the second subject 300 to induce the positive emotionalstate. The light signal may be an ambient light, a directed light or alaser beam. The light may be in a visible spectrum or an infrared lightIn all embodiments the second subject may the same as the first subject.

In certain embodiments, as shown in FIG. 40, brainwaves of the firstsubject being in a positive emotional state are recorded usingelectroencephalogram (EEG) 310. A temporal pattern is decoded from theEEG 320 and stored in a non-volatile memory 330. At a later time, thetemporal patter is retrieved from the non-volatile memory 340 andmodulated on an isotonic sound signal 350, which is projected to thesecond subject 360 to induce the positive emotional state. The isotonicsound signal may be imbedded in a music or an ambient noise. The soundmay be in an audible spectrum, infrasound or ultrasound.

In certain embodiments, as shown in FIG. 41, brainwaves of the firstsubject being in a positive emotional state are recorded usingelectroencephalogram (EEG) 370. A temporal spatial pattern is decodedfrom the EEG 380 and stored in a non-volatile memory 390. The first setof frequencies is computed by adding a predetermined delta to thefrequencies of the temporal frequency pattern 400. The second set offrequencies is computed by subtracting the delta from the frequencies ofthe temporal frequency pattern 410. The first set of frequencies ismodulated on the first acoustical signal 420. The second set offrequencies is modulated on the second acoustical signal 430. The firstacoustic signal is played into an ear of the second subject 440. Thesecond acoustic signal is played into another ear of the second subject450 thereby producing binaural stimulation to induce the positiveemotional state. The isotonic sound signal may be imbedded in a music oran ambient noise. The sound may be in an audible spectrum, infrasound orultrasound.

FIG. 42 shows graphs of tDCS. tRNS, and tACS stimulation patterns.

FIGS. 43 and 44 show representations of tDCS neural stimulation.

FIG. 45 shows a representation of tACS or tRNS neural stimulation.

FIG. 46 shows a representation of intracranial electrode implantation.

FIG. 47 shows a representation of tDCS electrode location.

Example 1

We record EEG of a first person (source) experiencing an emotionalarousal while seeing an authentic scenic view of nature (e.g., standingin front of the Grand Canyon, or Niagara Falls, or Giza Pyramids); thendecode the dynamic spatial and/or temporal patterns of the EEG andencode them in software. If a second person (recipient) wants toexperience the same emotional arousal while viewing a representation(e.g., a painting, a photograph or a video) of the same scenic view, thesoftware with an encoded dynamic temporal pattern is used to drive“smart bulbs” or another source of light and/or sound while the secondperson is viewing the representation of the scenic view. The result isan enhanced emotional response and a deeper immersive experience. SeeFIG. 1.

Example 2

We record EEG of an actor (or actress) while the actor (or actress) isplaying a particular role in a film or theatrical production; we thendecode the temporal patterns of the EEG and encode them in software. Ifanother person wants to experience enhanced emotional state while watchthe same film or a recording of the theatrical production, the softwarewith encoded temporal pattern is used to drive smart bulbs or anothersource of light and/or sound while the second person is watching thesame film or a recording of the theatrical production. The result is anenhanced emotional response and a deeper immersive experience.

Example 3

We record EEG of a first person (source) experiencing an emotionalarousal while engaged in an activity (playing a game, sports, etc.);then decode the dynamic spatial and/or temporal patterns of the EEG andencode them in software coupled with the virtual reality representationof the activity. If a second person (recipient) wants to experience thesame emotional arousal while viewing the virtual reality representationof the activity, the software with an encoded dynamic temporal patternis used to drive a current a current used in transcranial electric ormagnetic brain stimulation. The result is an enhanced emotional responseand a deeper immersive experience.

Example 4

A person is reading a book, and during the course of the reading, brainactivity, including electrical or magnetic activity, and optionallyother measurements, is acquired. The data is processed to determine thefrequency and phase, and dynamic changes of brainwave activity, as wellas the spatial location of emission. Based on a brain model, a set ofnon-invasive stimuli, which may include any and all senses, magneticnerve or brain stimulation, ultrasound, etc., is devised for a subjectwho is to read the same book. The set of non-invasive stimuli includesnot only content-based components, but also emotional responsecomponents. The subject is provided with the book to read, and thestimuli are presented to the subject synchronized with the progressthrough the book. Typically, the book is presented to the subjectthrough an electronic reader device, such as a computer or computingpad, to assist in synchronization. The same electronic reader device mayproduce the temporal pattern of stimulation across the various stimulusmodalities. The result is that the subject will be guided to the sameemotional states as the source of the target brain patterns.

In this description, several preferred embodiments were discussed.Persons skilled in the art will, undoubtedly, have other ideas as to howthe systems and methods described herein may be used. It is understoodthat this broad invention is not limited to the embodiments discussedherein. Rather, the invention is limited only by the following claims.

The aspects of the invention are intended to be separable and may beimplemented in combination, sub-combination, and with variouspermutations of embodiments. Therefore, the various disclosure herein,including that which is represented by acknowledged prior art, may becombined, sub-combined and permuted in accordance with the teachingshereof, without departing from the spirit and scope of the invention.

All references and information sources cited herein are expresslyincorporated herein by reference in their entirety.

What is claimed is:
 1. A method of inducing an emotional state in atarget subject, comprising: determining a desired emotional state;selecting a profile from a plurality of profiles stored in a memory, theplurality of profiles each corresponding to a brain activity pattern ofa donor subject having a respective emotional state; and exposing thetarget subject to at least one stimulus modulated according to theselected profile representing and being adapted to induce, in the targetsubject, the desired emotional state.
 2. The method of claim 1, whereinthe brain activity pattern is at least one of an electroencephalographicbrainwave pattern and a magnetoencephalographic brainwave pattern. 3.The method of claim 1, wherein said at least one stimulus stimulates acranial nerve of the target subject.
 4. The method of claim 1, whereinsaid at least one stimulus comprises at least one of a visual stimulus,and an auditory stimulus.
 5. The method of claim 4, wherein said atleast one stimulus comprises a two-channel auditory stimulus adapted toinduce binaural beats.
 6. The method of claim 1, wherein the stimuluscomprises at least one of a tactile stimulus and a proprioceptivestimulus.
 7. The method of claim 1, wherein the stimulus comprises an atleast one of a direct electrical current and an alternating electricalcurrent.
 8. The method of claim 1, wherein the stimulus comprises amagnetic field.
 9. The method of claim 1, wherein the desired emotionalstate is one of happiness, joy, gladness, cheerfulness, bliss, delight,ecstasy, optimism, exuberance, merriment, joviality; vivaciousness,pleasure, excitement, sexual arousal, relaxation, harmony, and peace.10. The method of claim 1, wherein the target subject is different fromthe donor subject.
 11. The method of claim 1, wherein the target subjectis identical with the donor subject and wherein the brain activitypattern of the donor subject was recorded prior to the exposing thetarget subject to at least one stimulus.
 12. The method of claim 1,wherein said at least one stimulus comprises a dynamically changingelectromagnetic field adapted to synchronize the target subjectsbrainwave pattern with a brainwave pattern of the donor subject havingthe desired emotional state.
 13. The method of claim 1, wherein theselected profile is derived from recording of brainwave patterns of thedonor subject selectively acquired during the desired emotional state.14. The method of claim 13, wherein the selected profile comprises amodel derived from at least one of a spatial, a frequency and a phaseanalysis of the recorded brainwave patterns.
 15. The method of claim 13,wherein the stimulus comprises at least one of an auditory stimulus anda visual stimulus with a frequency corresponding to at least a frequencypattern in a brainwave pattern of the donor subject.
 16. The method ofclaim 1, further comprising: recording EEG signals of the donor subjectin the desired emotional state; decoding at least one of a temporal anda spatial pattern from the recorded EEG signals; and storing the decodedat least one of temporal and spatial pattern in a non-volatile memory asat least one profile.
 17. The method of claim 1, further comprisingselectively modifying stimulus based on differences between the donorsubject, from which the profile is derived, and the target subject. 18.The method of claim 1, wherein the stimulus comprises applying at leastone of a temporal and a spatial electrical stimulation pattern to thetarget subject via transcranial electrical stimulation (TES) to inducethe desired emotional state.
 19. The method of claim 18, wherein thetranscranial electrical stimulation (TES) is at least one of atranscranial direct current stimulation (tDCS), an oscillatingtranscranial direct current stimulation (osc-tDCS), a transcranialalternating current stimulation (tACS), a transcranial pulsed currentstimulation (tPCS), and a transcranial random noise stimulation (tRNS).20. The method of claim 1, wherein the profile is derived from brainactivity pattern of the donor subject comprising a magnetoencephalogram(MEG), and the stimulus comprises applying a spatial magneticstimulation pattern to the target subject via transcranial magneticstimulation (TMS) to induce the desired emotional state.
 21. The methodof claim 1, wherein the stimulus achieves brain entrainment in thetarget subject.
 22. The method of claim 1, further comprisingdetermining a second desired emotional state; selecting a second profilefrom the plurality of profiles stored in a memory; and exposing thetarget subject to a stimulus modulated according to the selected secondprofile, representing and being adapted to induce, in the targetsubject, the second desired emotional state, the second emotional statebeing different from the emotional state and being induced in successionafter the emotional state.
 23. The method of claim 1, wherein at leastone profile corresponds to consensus brain activity pattern of aplurality of donor subjects, each of the plurality of donor subjectshaving the respective emotional state.
 24. A method of brainwaveentrainment with a brainwave pattern stored in a memory, the brainwavepattern representing at least one of a temporal and spatial decodedpattern from brainwaves of a first subject while in a desired emotionalstate, comprising: retrieving said pattern from the memory; modulatingthe decoded at least one of the temporal and the spatial pattern on atleast one stimulus signal; and applying said at least one stimulussignal to a second subject, to induce the second subject to assume theemotional state.
 25. The method of claim 24, wherein the step ofrecording brainwaves comprise recording of at least one ofelectroencephalogram and magnetoencephalogram of the brainwaves.
 26. Themethod of claim 24, wherein the stimulus signal is at least one of adirect current and an alternating current and said applying comprisesapplying said at least one of a direct current and an alternatingcurrent to the second subject via respectively a transcranial directcurrent stimulation (tDCS) or transcranial alternating currentstimulation (tACS) to induce the desired emotional state.
 27. The methodof claim 24, wherein said modulating comprises modulating the at leastone of the temporal and spatial pattern on at least one of a current, amagnetic field, a light signal, and an acoustic signal.
 28. A system forbrainwave entrainment, comprising: a memory, configured to store abrainwave pattern representing at least one of a temporal and spatialdecoded pattern from brainwaves of a first subject while in a desiredemotional state; at least one processor configured to modulate thedecoded at least one of the temporal and the spatial pattern on at leastone stimulus signal; and a stimulator configured to apply the at leastone stimulus signal to a second subject, to induce the second subject toassume the emotional state.
 29. The method of claim 28, wherein thestimulator comprises at least one of a current stimulator, a magneticstimulator, a light signal stimulator, and an acoustic stimulator.